Archives, Aesthetic Dimensions, and Academic Identity
Bibliographic record
Abstract
Abstract: Three authors offer a methodological bricolage to explore visual, textual, and poetic archives that shape our identities as women academics. We draw on aspects of autoethnography, reflexively engaged, to explore these archives as they lend insight into the construction of personal identity. Understanding both the archives and identity as inseparable from culture, we take up cultural analysis as historical method as we examine, through archival metaphor, the historical struggles of women in academia. We believe that scholarly writing within disciplinary contexts is one way women constitute identity for ourselves and, in so doing, find pathways to write our way into existence. Keywords: Women scholars; academic identity; autoethnography; arts-informed research. Résumé : Trois autrices proposent un bricolage méthodologique pour explorer des archives visuelles, textuelles et poétiques qui ont façonné nos identités en tant que femmes universitaires. Nous nous appuyons sur des aspects d’auto-ethnographie, dans un mode de réflexivité, pour étudier ces archives et la lumière qu’elles jettent sur la construction de l’identité personnelle. Considérant archives et identité indissociables de la culture, nous utilisons l’analyse culturelle comme méthode historique pour étudier, par l’entremise de la métaphore archivistique, les luttes historiques des femmes en milieu universitaire. Nous croyons que les écrits érudits dans un contexte disciplinaire sont un moyen pour les femmes de construire leur identité et, par le fait même, d’identifier des avenues pour trouver leur véritable place dans la vie. Mots-clés : femmes érudites, identité académique, autoethnographie, Recherche fondée sur les arts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".