Supporting Collaborative Reflections: Case Writing in an Urban Professional Development School

Teaching often does not include the opportunity to share with a colleague its joy and despair; how to address the multitude of split second decisions you must make on a daily basis; what to do when a lesson fails; how to address the concerns of an irate parents; or where to find resources when needed.... Time works against us.... Hallways and lounge conversations aren\u27t adequate. Through case writing, we have finally found the precious time to reflect on our experiences

Review Essay: Janet Halley, Split Decisions: How and Why to Take a Break from Feminism

[Excerpt] “My overarching reaction to Janet Halley\u27s recent book, Split Decisions: How and Why to Take a Break from Feminism, can be summarized with a one sentence cliché: The perfect is the enemy of the good.\u27 She holds feminism to a standard of perfection no human endeavor could possibly meet, and then heartily criticizes it for falling short. Though Halley\u27s myriad observations about feminism occasionally resonated with my own views and experiences, ultimately I remain unconvinced that taking a break from feminism would, for me, be either justified or productive. But I did (mostly) enjoy reading it. Halley is well read, cleverly provocative, and a gifted writer. Below I give a somewhat glib and superficial overview of the book, and my reactions to it. I explain why I think Halley is too hard on feminists generally, and on Catharine MacKinnon specifically. And I take her to task for being harshly critical of feminism without offering realistic, pragmatic, or lawyerly alternatives. You can\u27t theorize your way into an abortion, or out of a rape. You can have to rely on a legal system that may fail you, in which case you can work to improve it so that others don\u27t suffer as you did. This is part of the very essence of feminism, which Halley gives short shrift.

Guided Proofreading of Automatic Segmentations for Connectomics

Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual search for these errors as the bottleneck in interactive proofreading. To aid error correction, we develop two classifiers that automatically recommend candidate merges and splits to the user. These classifiers use a convolutional neural network (CNN) that has been trained with errors in automatic segmentations against expert-labeled ground truth. Our classifiers detect potentially-erroneous regions by considering a large context region around a segmentation boundary. Corrections can then be performed by a user with yes/no decisions, which reduces variation of information 7.5x faster than previous proofreading methods. We also present a fully-automatic mode that uses a probability threshold to make merge/split decisions. Extensive experiments using the automatic approach and comparing performance of novice and expert users demonstrate that our method performs favorably against state-of-the-art proofreading methods on different connectomics datasets.Comment: Supplemental material available at http://rhoana.org/guidedproofreading/supplemental.pd

Buying Time: Real and Hypothetical Offers

This paper provides the results of a field test of contingent valuation estimates within a willingness to accept framework. Using dichotomous choice questions in telephone-mail-telephone interviews, we compare responses to real and hypothetical offers to survey respondents for the opportunity to spend time in a second set of interviews on an undisclosed topic. Five hundred and forty people were randomly split between the real and hypothetical treatments. Our findings indicate no significant differences between people's choices with real and hypothetical offers. Choice models indicate the size of the offer and income were significant determinants of respondents' decisions, and these models were not significantly different between real and hypothetical offers.

Beyond Baby-Splitting: Arbitrator Decision-Making Patterns in Employment Cases

That arbitrators tend to “split the baby” by issuing compromise awards is amongst the hoariest of clichés in the dispute resolution field. While the idea of arbitrators as baby-splitters has been challenged by commentators and lacks support in empirical evidence, the idea is surprisingly persistent. More importantly, it may be continuing to influence the decisions of actors whether or not to use arbitration to resolve disputes. A 1997 survey conducted by David Lipsky, Ronald Seeber, and Richard Fincher found that 49.7% of general counsels of Fortune 1000 corporations reported that concerns about compromise decisions was one of their reasons for not using arbitration

Inefficient Worker Turnover

This paper considers the efficiency properties of risk-neutral workers’ mobility decisions in an equilibrium model with search frictions, but no search externalities, when the rent accruing to a match is split through bargaining. Matches are ex ante homogeneous and their true productivity is learnt after the match is formed. It is shown that the efficiency of worker turnover depends on contract enforceability, and that in the absence of complete enforceability the equilibrium fails to be efficient. This is because without complete enforceability firms cannot credibly offer workers contracts that will guarantee them the entire future of all potential future matches.On-the-Job Search; Learning; Bargaining; Contracts; Enforceability

Single machine scheduling with general positional deterioration and rate-modifying maintenance

We present polynomial-time algorithms for single machine problems with generalized positional deterioration effects and machine maintenance. The decisions should be taken regarding possible sequences of jobs and on the number of maintenance activities to be included into a schedule in order to minimize the overall makespan. We deal with general non-decreasing functions to represent deterioration rates of job processing times. Another novel extension of existing models is our assumption that a maintenance activity does not necessarily fully restore the machine to its original perfect state. In the resulting schedules, the jobs are split into groups, a particular group to be sequenced after a particular maintenance period, and the actual processing time of a job is affected by the group that job is placed into and its position within the group