Bibliographic record
Abstract
In this article, we address issues of attribution, utility, and accountability in ethnographic research. We examine the two main analytical approaches that have structured the debate on data collection and theorization in ethnography over the last five decades: an inductivist approach, with grounded theory as its main analytic strategy; and a deductivist stance, which uses field sites to explore empirical anomalies that enable an ethnographer to test and build upon pre-existing theories. We engage recent reformulations of this classical debate, with a specific focus on abductive and reflexive approaches in ethnography, and then weigh into these debates, ourselves. drawing on our own experiences producing and using research in non-academic settings. In so doing, we highlight the importance of strategy and accountability in one's ethnographic practices and accounts, advocating for an approach to ethnographic research that is reflexive and overtly responsive to the knowledge needs and change goals articulated by non-academic collaborators. Ultimately, we argue for a research stance that we describe as tactical responsivity, whereby researchers work with key collaborators and stakeholders to identify the strategic aims and audiences for their research, and develop ethnographic, analytic, and communicative practices that enable them to generate and mobilize the knowledge required to actualize their shared aims.
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.
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 | 0.125 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".