Stumbling Around: Towards an Approach to Institutional Ethnography as a Frame of Inquiry into Library Work
Bibliographic record
Abstract
This paper focuses on the researcher’s experience developing an approach to institutional ethnography to examine library work in a Midwestern USA middle school. Throughout the year-and-a-half process of developing an approach to the methodology, gaining access, collecting data, analyzing data, and writing the dissertation, I kept various journals and writings showing how I learned to see the library ethnographically.Cette communication porte sur l’expérience de la chercheure lors du développement d’une approche d’ethnographie institutionnelle afin d’évaluer le travail bibliothéconomique dans une école intermédiaire du Midwest des États-Unis. Durant le processus d’un an et demi qu’a nécessité le développement de la méthodologie, l’accès à l’institution, la collecte et l’analyse de données et la rédaction de la thèse, j’ai consigné par écrit comment j’ai appris à concevoir la bibliothèque d’un point de vue ethnographique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.055 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".