Bibliographic record
Abstract
In applied ethnographic praxis, how should we use theory? Exploring how existing theory from a variety of domains has supported and advanced our work, this paper justifies and demonstrates how theory can be used in an accessible and practical manner when framing research and analyzing experience in the field. Two approaches for using theory are outlined, providing guidelines for different ways to apply theory to applied ethnography. Defense of such approaches is provided through both an appeal to the value we have seen it add to ethnography in industry and to a brief return to Hermeneutic ethnography, inspired by the likes of Gadamer and Geertz. The latter serves as a reminder of reasons to be skeptical that as ethnographers we uncover “the real.” Pre‐existing theory provides valuable assistance when transforming an insight about the world into an idea with explanatory and predictive potential for our clients. Drawing upon theory allows us to elevate an interesting description of the “real” world into actionable insights with theoretical muscle. And we contend that ethnographers in industry need not incorporate theory in their work in the manner that is typical of academia – the same ‘rules’ and norms do not apply.
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 | 0.160 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".