Caught in the Act: An Autoethnographic Analysis of the Performance of Information Literacy Instruction
Bibliographic record
Abstract
What factors comprise a librarian’s performance within the act of conducting in-person information literacy instruction? This paper describes an autoethnographic exploration of this question, grounded in Erving Goffman’s dramaturgy and Michael Kirby’s matrix approach to acting and non-acting. The author argues that a more sophisticated performance-oriented understanding of instruction could benefit librarians preparing to teach. This work explores a complex phenomenon that has not yet been described: the performance of information literacy instruction.Quels facteurs relèvent du rôle du bibliothécaire lors de l’enseignement de la maîtrise informationnelle en personne? Cette communication présente une étude exploratoire autoethnographique de la question, ancrée dans la dramaturgie d’Erving Goffman et l’approche matricielle de Michael Kirby au jeu et au non-jeu. L’auteure affirme qu’une compréhension de l’enseignement plus sophistiquée et axée sur le jeu serait bénéfique pour les bibliothécaires qui s’apprêtent à donner leur formation. L’étude explore un phénomène complexe qui n’a pas encore été décrit : la performance lors de l’enseignement de la maîtrise informationnelle.***Practitioner to CAIS/ACSI Award Winner***
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".