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Record W3101753443 · doi:10.1002/jhbs.22069

Doing history that matters: Going public and activating voices as a form of historical activism

2020· article· en· W3101753443 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the History of the Behavioral Sciences · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPrivilege (computing)AnecdoteExperiential learningPower (physics)Experiential knowledgeHeterosexismSociologyPublic relationsAestheticsMedia studiesPolitical scienceGender studiesEpistemologyLawPedagogyArtLesbian

Abstract

fetched live from OpenAlex

For many of us academics, doing community-engaged research means coming to terms with the significant gaps in experience, privilege, and power, and overall access to knowledge. We are trained to learn through texts, not through direct experience. In some ways, we are even conditioned to tune out experience, or anecdote, to dilute personal subjectivities in favor of a critical analysis informed by a combination of methods and sources, and a reliance on text-based forms of evidence. Whereas for most community members, evidence is experiential. This dynamic also underscores the tremendous power and responsibility we have as historians to shape identities and legacies through the stories we tell. In the end, I believe the risks are worth the rewards.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.161
GPT teacher head0.276
Teacher spread0.115 · 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