Preface Chapter 1 The challenge Chapter 2 The nature of the evidence: A synthesis of meta-analyses Chapter 3 The argument: Visible teaching and visible learning Chapter 4: The contributions from the student Chapter 5 The contributions from the home Chapter 6 The contributions from the school Chapter 7 The contributions from the teacher Chapter 8 The contributions from the curricula Chapter 9 The contributions from teaching approaches - I Chapter 10 The contributions from teaching approaches - II Chapter 11: Bringing it all together Appendix A: The 800 meta-analyses Appendix B: The meta-analyses by rank order References

Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement

American Bankruptcy Institute Law Review 17 Am. Bankr. Inst. L. Rev., No. 1, Spring, 2009. Boston College Law Review 50 B.C. L. Rev., No. 3, May, 2009. Boston University Public Interest Law Journal 18 B.U. Pub. Int. L.J., No. 2, Spring, 2009. Cardozo Journal of Conflict Resolution 10 Cardozo J. Conflict Resol., No. 2, Spring, 2009. Cardozo Public Law, Policy, & Ethics Journal 7 Cardozo Pub. L. Pol’y & Ethics J., No. 3, Summer, 2009. Chicago Journal of International Law 10 Chi. J. Int’l L., No. 1, Summer, 2009. Colorado Journal of International Environmental Law and Policy 20 Colo. J. Int’l Envtl. L. & Pol’y, No. 2, Winter, 2009. Columbia Journal of Law & the Arts 32 Colum. J.L. & Arts, No. 3, Spring, 2009. Connecticut Public Interest Law Journal 8 Conn. Pub. Int. L.J., No. 2, Spring-Summer, 2009. Cornell Journal of Law and Public Policy 18 Cornell J.L. & Pub. Pol’y, No. 1, Fall, 2008. Cornell Law Review 94 Cornell L. Rev., No. 5, July, 2009. Creighton Law Review 42 Creighton L. Rev., No. 3, April, 2009. Criminal Law Forum 20 Crim. L. Forum, Nos. 2-3, Pp. 173-394, 2009. Delaware Journal of Corporate Law 34 Del. J. Corp. L., No. 2, Pp. 433-754, 2009. Environmental Law Reporter News & Analysis 39 Envtl. L. Rep. News & Analysis, No. 7, July, 2009. European Journal of International Law 20 Eur. J. Int’l L., No. 2, April, 2009. Family Law Quarterly 43 Fam. L.Q., No. 1, Spring, 2009. Georgetown Journal of International Law 40 Geo. J. Int’l L., No. 3, Spring, 2009. Georgetown Journal of Legal Ethics 22 Geo. J. Legal Ethics, No. 2, Spring, 2009. Golden Gate University Law Review 39 Golden Gate U. L. Rev., No. 2, Winter, 2009. Harvard Environmental Law Review 33 Harv. Envtl. L. Rev., No. 2, Pp. 297-608, 2009. International Review of Law and Economics 29 Int’l Rev. L. & Econ., No. 1, March, 2009. Journal of Environmental Law and Litigation 24 J. Envtl. L. & Litig., No. 1, Pp. 1-201, 2009. Journal of Legislation 34 J. Legis., No. 1, Pp. 1-98, 2008. Journal of Technology Law & Policy 14 J. Tech. L. & Pol’y, No. 1, June, 2009. Labor Lawyer 24 Lab. Law., No. 3, Winter/Spring, 2009. Michigan Journal of International Law 30 Mich. J. Int’l L., No. 3, Spring, 2009. New Criminal Law Review 12 New Crim. L. Rev., No. 2, Spring, 2009. Northern Kentucky Law Review 36 N. Ky. L. Rev., No. 4, Pp. 445-654, 2009. Ohio Northern University Law Review 35 Ohio N.U. L. Rev., No. 2, Pp. 445-886, 2009. Pace Law Review 29 Pace L. Rev., No. 3, Spring, 2009. Quinnipiac Health Law Journal 12 Quinnipiac Health L.J., No. 2, Pp. 209-332, 2008-2009. Real Property, Trust and Estate Law Journal 44 Real Prop. Tr. & Est. L.J., No. 1, Spring, 2009. Rutgers Race and the Law Review 10 Rutgers Race & L. Rev., No. 2, Pp. 441-629, 2009. San Diego Law Review 46 San Diego L. Rev., No. 2, Spring, 2009. Seton Hall Law Review 39 Seton Hall L. Rev., No. 3, Pp. 725-1102, 2009. Southern California Interdisciplinary Law Journal 18 S. Cal. Interdisc. L.J., No. 3, Spring, 2009. Stanford Environmental Law Journal 28 Stan. Envtl. L.J., No. 3, July, 2009. Tulsa Law Review 44 Tulsa L. Rev., No. 2, Winter, 2008. UMKC Law Review 77 UMKC L. Rev., No. 4, Summer, 2009. Washburn Law Journal 48 Washburn L.J., No. 3, Spring, 2009. Washington University Global Studies Law Review 8 Wash. U. Global Stud. L. Rev., No. 3, Pp.451-617, 2009. Washington University Journal of Law & Policy 29 Wash. U. J.L. & Pol’y, Pp. 1-401, 2009. Washington University Law Review 86 Wash. U. L. Rev., No. 6, Pp. 1273-1521, 2009. William Mitchell Law Review 35 Wm. Mitchell L. Rev., No. 4, Pp. 1235-1609, 2009. Yale Journal of International Law 34 Yale J. Int’l L., No. 2, Summer, 2009. Yale Journal on Regulation 26 Yale J. on Reg., No. 2, Summer, 2009.

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Current index to legal periodicals

This book presents a simple geometric model of voting as a tool to analyze parliamentary roll call data. Each legislator is represented by one point and each roll call is represented by two points that correspond to the policy consequences of voting Yea or Nay. On every roll call each legislator votes for the closer outcome point, at least probabilistically. These points form a spatial map that summarizes the roll calls. In this sense a spatial map is much like a road map because it visually depicts the political world of a legislature. The closeness of two legislators on the map shows how similar their voting records are, and the distribution of legislators shows what the dimensions are. These maps can be used to study a wide variety of topics including how political parties evolve over time, the existence of sophisticated voting and how an executive influences legislative outcomes.

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Spatial Models of Parliamentary Voting

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that "All models are wrong but some are useful." We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly.

Explaining Explanations in AI

Teeming with chatrooms, online discussion groups, and blogs, the Internet offers previously unimagined opportunities for personal expression and communication. But theres a dark side to the story. A trail of information fragments about us is forever preserved on the Internet, instantly available in a Google search. A permanent chronicle of our private livesoften of dubious reliability and sometimes totally falsewill follow us wherever we go, accessible to friends, strangers, dates, employers, neighbors, relatives, and anyone else who cares to look. This engrossing book, brimming with amazing examples of gossip, slander, and rumor on the Internet, explores the profound implications of the online collision between free speech and privacy.Daniel Solove, an authority on information privacy law, offers a fascinating account of how the Internet is transforming gossip, the way we shame others, and our ability to protect our own reputations. Focusing on blogs, Internet communities, cybermobs, and other current trends, he shows that, ironically, the unconstrained flow of information on the Internet may impede opportunities for self-development and freedom. Long-standing notions of privacy need review, the author contends: unless we establish a balancebetweenprivacy and free speech, we may discover that the freedom of the Internet makes us less free. (11/01/2007)

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The Future of Reputation: Gossip, Rumor, and Privacy on the Internet

This Essay reports the results of an interdisciplinary project comparing political science and legal approaches to forecasting Supreme Court decisions. For every argued case during the 2002 Term, we obtained predictions of the outcome prior to oral argument using two methods—one a statistical model that relies on general case characteristics, and the other a set of independent predictions by legal specialists. The basic result is that the statistical model did better than the legal experts in forecasting the outcomes of the Term’s cases: The model predicted 75% of the Court’s affirm/reverse results correctly, while the experts collectively got 59.1% right. These results are notable, given that the statistical model disregards information about the specific law or facts of the cases. The model’s relative success was due in large part to its ability to predict more accurately the important votes of the moderate Justices (Kennedy and O’Connor) at the center of the current Court. The legal experts, by contrast, did best at predicting the votes of the more ideologically extreme Justices, but had difficulty predicting the centrist Justices. The relative success of the two methods also varied by issue area, with the statistical model doing particularly well in forecasting “economic activity” cases, while the experts did comparatively better in the “judicial power” cases. In addition to reporting the results in detail, the Essay explains the differing methods

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The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decisionmaking

Political scientists and legal academics have long scrutinized the U.S. Supreme Court's work to understand what motivates the justices. Despite significant differences in methodology, both disciplines seek to explain the Court's decisions by focusing on examining past cases. This retrospective orientation is surprising. In other areas of government, for example, presidential elections and congressional decision making, political scientists engage in systematic efforts to predict outcomes, yet few have done this for court decisions. Legal academics, too, possess expertise that should enable them to forecast legal events with some accuracy. After all, the everyday practice of law requires lawyers to predict court decisions in order to advise clients or determine litigation strategies.The authors thank Michael Cherba, Nancy Cummings, David Dailey, Alison Garvey, Nick Hershman, and Robin Rimmer for their assistance. Their project is supported in part by National Science Foundation grants SES-0135855 and SES 0136679. The foundation bears no responsibility for the results or conclusions.

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Competing Approaches to Predicting Supreme Court Decision Making

This Article presents empirical evidence on an issue at the heart of the academic controversy over the at-will rule -- namely, whether employees have sufficient information to effectively negotiate issues of job security. Using a written survey to collect data from several hundred workers, the study documents a widespread misunderstanding of the basic default rule of employment at will. The results indicate that workers consistently overestimate their legal rights, with overwhelming majorities (as high as 89%) believing that they are legally protected against arbitrary and unjust discharges when in fact they can be dismissed at will. These findings directly contradict a crucial assumption made by Richard Epstein and other defenders of the at-will rule: the assumption that both employers and employees understand the grounds on which they contract. One of the chief virtues of the at-will rule, according to its defenders, is that it is merely a default rule; the parties remain free to contract around it. If, however, workers are unaware of the legal default rule, then their failure to contract for greater job security can no longer be presumed to represent their true preferences. The presence of a significant information failure of this sort casts doubt on the ability of workers to protect their interests, suggesting a breakdown in the bargaining process. Thus, these findings undermine the traditional economic argument that the market can be relied upon to produce an efficient outcome when it comes to job security terms.

Bargaining with Imperfect Information: A Study of Worker Perceptions of Legal Protection in an At-Will World

A data revolution is transforming the workplace. Employers are increasingly relying on algorithms to decide who gets interviewed, hired or promoted. Proponents of the new data science claim that automated decision systems can make better decisions faster, and are also fairer, because they replace biased human decision-makers with “neutral” data. However, data are not neutral and algorithms can discriminate. The legal world has not yet grappled with these challenges to workplace equality. The risks posed by data analytics call for fundamentally rethinking anti-discrimination doctrine. When decision-making algorithms produce biased outcomes, they may seem to resemble familiar disparate impact cases, but that doctrine turns out to be a poor fit. Developed in a different context, disparate impact doctrine fails to address the ways in which algorithms can introduce bias and cause harm. This Article argues instead for a plausible, revisionist interpretation of Title VII, in which disparate treatment and disparate impact are not the only recognized forms of discrimination. A close reading of the text suggests that Title VII also prohibits classification bias — namely, the use of classification schemes that have the effect of exacerbating inequality or disadvantage along the lines of race or other protected category. This description matches well the concerns raised by workplace analytics. Framing the problem in terms of classification bias leads to some quite different conclusions about how the anti-discrimination norm should be applied to algorithms, suggesting both the possibilities and limits of Title VII’s liability focused model.

Data-Driven Discrimination at Work

Recent studies have established that decision-making by federal court of appeals judges is influenced not only by the preferences of the judge, but also the preferences of her panel colleagues. Although the existence of these panel effects is well documented, the reasons they occur are less well understood. Scholars have proposed a number of competing theories to explain panel effects, but none has been established empirically. In this Article, I report an empirical test of two competing explanations of panel effects - one emphasizing deliberation internal to a circuit panel, the other hypothesizing strategic behavior on the part of circuit judges. The latter explanation posits that court of appeals judges act strategically in light of the expected actions of others, and that therefore, panel effects should depend upon how the preferences of the Supreme Court or the circuit en banc are aligned relative to those of the panel members. Analyzing votes in Title VII sex discrimination cases, I find no support for the theory that panel effects are caused by strategic behavior aimed at inducing or avoiding Supreme Court review. On the other hand, the findings strongly suggest that panel effects are influenced by circuit preferences. Both minority and majority judges on ideologically mixed panels differ in their voting behavior depending upon how the preferences of the circuit as a whole are aligned relative to the panel members. This study provides evidence that panel effects do not result from a dynamic wholly internal to the three judges hearing a case, but are influenced by the environment in the circuit as a whole as well.

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Pauline T. Kim

Papers

The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decisionmaking

Competing Approaches to Predicting Supreme Court Decision Making

Bargaining with Imperfect Information: A Study of Worker Perceptions of Legal Protection in an At-Will World

Data-Driven Discrimination at Work

Deliberation and Strategy on the United States Courts of Appeals: An Empirical Exploration of Panel Effects