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Record W4385598710 · doi:10.59962/9780774861830-003

Acknowledgments

2019· book-chapter· en· W4385598710 on OpenAlexfundno aff

Bibliographic record

VenueUniversity of British Columbia Press eBooks · 2019
Typebook-chapter
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaAustralian National University
KeywordsGeography

Abstract

fetched live from OpenAlex

A number of people and organizations made it possible to bring this book to fruition.First, I would like to thank all the authors for agreeing to contribute in 2015, for writing groundbreaking texts, for reviewing their chapters over and over in light of the many rounds of revisions, and for maintaining their confidence in the project despite the many hurdles it has encountered.I remain convinced that Queering Representation is worth all the eff ort!I am grateful to the reviewers for the time they devoted to reading and commenting on the various draft s of the manuscript.Th eir comments helped us to produce a book that meets the highest standards.A special thanks to Reverend Dr. Cheri DiNovo for writing the Foreword of this book.I fi nd it particularly important that she authored the Foreword because her dedication to LGBTQ people and communities for decades is simply exceptional.She signed the "We Demand" brief, submitted to the federal government in 1971 (she is the only woman signatory), and was a member of the Ontario Legislative Assembly from 2006 to 2017.In this respect, she is living proof that any substantial change in the daily citizenship of LGBTQ people requires that social activism and institutional politics work hand-in-hand.Th e Introduction and the chapter of which I am the sole author benefi tted from Käthe Roth's translation skills.I thank her for this, especially for her constructive guidance, which oft en goes beyond simple translation.Some of the chapters in Queering Representation were discussed at the 2016 Canadian Political Science Association Congress in Calgary.My thanks go to the colleagues who participated in this event, as well as to the audience for their comments and their enthusiasm for what was then a book project.I worked on this manuscript while I was a visiting researcher at the Australian National University, School of Politics and International Relations, from January to April 2018.I can never express how much I love being at the ANU, where I can work in utter peace and in a

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)0.005
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.812
Threshold uncertainty score0.630

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.1880.105

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 head0.029
GPT teacher head0.248
Teacher spread0.219 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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