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Record W4378697717 · doi:10.3726/b20659

Between Complicity and Integrity

2023· book· en· W4378697717 on OpenAlexaboutno aff
Nora Timmerman

Bibliographic record

VenuePeter Lang Verlag eBooks · 2023
Typebook
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsnot available
Fundersnot available
KeywordsComplicityEnvironmental ethicsSociologyAestheticsMedia studiesPolitical scienceLawArt

Abstract

fetched live from OpenAlex

What does it look like to live with integrity in the midst of complicity? When the daily tasks of eating, working, and providing for our homes are tangled up with climate change, exploitative working conditions, colonial legacies of stolen land, and more, what do we do? Based on five years of research, this book shares intimate narratives about 12 educators working at the intersection of education, environment, and social change, as they describe their understandings and experiences of integrity and complicity in today’s world. Nora Timmerman argues that these stories collectively teach us how scale matters, to stop being one person, and to act anyway. Integrity comes not from ridding oneself of complicity but from critical learning, community accountability, cultivating interdependence, and strategic experimentation to create new worlds. "This delightful book, Between Integrity and Complicity, is an environmental educator’s manifesto on how to live well in relation to communities of others. The stories call on us to do more acting (with inevitable missteps) and less worrying. The author skillfully untangles the roots of complicity, integrity and suffering to show us the colourfully varied microcosm of ecological and social renewal, perseverance, and possibility. This book honours listening to the world in all its myriad ways—it is a found treasure." —Leesa Fawcett, PhD, Environmental and Urban Change, Coordinator of Environmental & Sustainability Education, York University "This book explores the existential journeys of leading environmental educators and scholars through illuminating portraits and vignettes in a thoughtfully nuanced manner. It is a timely and notable contribution to the literature." —Greg Lowan-Trudeau, PhD, Associate Professor of Education, University of Calgary

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)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0150.066
Scholarly communication0.0160.015
Open science0.0010.019
Research integrity0.0030.012
Insufficient payload (model declined to judge)0.0070.001

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 head0.066
GPT teacher head0.323
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2023
Admission routes1
Has abstractyes

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