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List of Contributors

2015· other· en· W4249449457 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsKnightState (computer science)Library science

Abstract

fetched live from OpenAlex

Citation (2015), "List of Contributors", Promoting and Sustaining a Quality Teacher Workforce (International Perspectives on Education and Society, Vol. 27), Emerald Group Publishing Limited, Bingley, pp. ix-x. https://doi.org/10.1108/S1479-367920140000027019 Publisher: Emerald Group Publishing Limited Copyright © 2015 Emerald Group Publishing Limited Motoko Akiba Florida State University, Tallahassee, FL, USA Björn Åstrand Karlstad University, Karlstad, Sweden Kira J. Baker-Doyle Arcadia University, Glenside, PA, USA Kristina Brezicha The Pennsylvania State University, University Park, PA, USA Richard A. Duschl The Pennsylvania State University, University Park, PA, USA Pablo Fraser Pennsylvania State University, University Park, PA, USA Edward J. Fuller The Pennsylvania State University, University Park, PA, USA Karen Hammerness American Museum of Natural History, New York, NY, USA Sakiko Ikoma The Pennsylvania State University, University Park, PA, USA Jihyun Kim Michigan State University, East Lansing, MI, USA Kirsti Klette University of Oslo, Oslo, Norway Stephanie L. Knight The Pennsylvania State University, University Park, PA, USA Gerald K. LeTendre Pennsylvania State University, University Park, PA, USA Yang Lu ACT, Inc., Washington, DC, USA James Pippin Michigan State University, East Lansing, MI, USA Michael Rodriguez University of Minnesota, Minneapolis, MN, USA Lindsey Schrott The Pennsylvania State University, University Park, PA, USA Kent Seidel University of Colorado Denver, Denver, CO, USA Armend Tahirsylaj The Pennsylvania State University, University Park, PA, USA Maria Teresa Tatto Michigan State University, East Lansing, MI, USA Peter Wallet UNESCO Institute for Statistics, Montreal, Quebec, Canada Jennifer Whitcomb University of Colorado at Boulder, Boulder, CO, USA Alexander W. Wiseman Lehigh University, Bethlehem, PA, USA Peter Youngs University of Virginia, Charlottesville, VA, USA Book Chapters Promoting and Sustaining a Quality Teacher Workforce International Perspectives on Education and Society Promoting and Sustaining a Quality Teacher Workforce Copyright Page List of Contributors Introduction: The Challenges of Teacher Effectiveness and Quality Worldwide The UNESCO Institute for Statistics (UIS) Strategy on Teacher Statistics: Developing Effective Measures of Quantity and Quality in Education Measuring Teachers’ Professional Learning Activities in International Context Regimes of Teacher Beliefs from a Comparative and International Perspective Unpacking Teacher Differences in Didaktik and Curriculum Traditions: Trends from TIMSS 2003, 2007, and 2011 Exploring Novice Teachers’ Core Competencies Indicators of Quality in Teacher Education: Looking at Features of Teacher Education from an International Perspective The Influence of Teacher Education on Mathematics Teaching Knowledge: Local Implementation of Global Ideals Building and Sustaining a Quality STEM Teacher Workforce: Access to Instructional Leadership and the Interruptions of Educator Turnover No Teacher Is an Island: How Social Networks Shape Teacher Quality Conceptual Understandings of Democracy and Values as Aspects of Teacher Quality – The Case of Teacher Education in Sweden Teachers’ Responses to Changes in Teacher Evaluation Policy in Korea and the United States Current Trends in the Framing of Teacher Qualities World Culture and Teacher Quality: Consensus, Convergence, and Conflict About the Authors Index

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.263
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.007
Science and technology studies0.0040.001
Scholarly communication0.0130.007
Open science0.0040.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.7370.749

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.373
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2015
Admission routes1
Has abstractyes

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