Narratives of irony and failure in ethnographic work
Bibliographic record
Abstract
Abstract Organizational ethnography is one of the most valued approaches to qualitative studies of organizations. Much attention has been given to the development of the research process, of which the researcher's identity is an integral part. However, we believe that the analysis of research failures has been much less developed in the discourse of ethnographic methods for the study of organizations. Therefore, we have explored some of the “slips” in ethnographic work, as described in accounts of fellow organizational anthropologists. As the study is qualitative, we have adopted a narrative research method. We have divided the “slips” (i.e., errors) into four categories important for the ethnographer's identity: (a) one's role; (b) one's project, (c) one's relation to “the Other”; and (d) the social context of the slip. Copyright © 2010 ASAC. Published by John Wiley & Sons, Ltd.
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 | 0.044 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.036 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| 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".