Bibliographic record
Abstract
Institutional ethnography (IE) is an innovative approach to research that requires a significant shift in researchers’ ordinary habits of thinking. There is a growing body of methodological resources for IE researchers however advice about how to proceed with analysis remains somewhat scattered and cryptic. The purpose of the first of a two-paper series is to contribute to publications focused exclusively on analysis. The aim is to provide practical tips to support researchers to shift their ordinary habits of thinking. This first paper outlines how this must happen at the outset of the research design. Analysis of the phenomenon under study commences as the research is being formulated. The approaches to analytical thinking outlined in this paper are based on my own IE research and also my experience working with graduate students since 2008. In this first volume of the two-paper set I provide a brief background to the method and direct readers to important IE resources. I outline three core methodological concepts: standpoint, problematic and ruling relations. I discuss how these concepts guide the early analytical thinking that is embedded in the research design and the critical analysis of the literature that is part of the process of analysis in IE. The second paper provides practical advice for working with data.
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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 | 0.169 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".