Bibliographic record
Abstract
For you to trust the results of an experimental study, you need to be satisfied that the researchers took all necessary precautions to keep their personal biases and expectations in check. You want to be sure that the differences between the experimental and control groups can be reliably explained as a measure of the effects of the independent variable (or, in clinician-speak, the intervention), not as an artifact of what the researchers hoped to find. With such studies, the more the self-of-the-researcher disappears into the background, the better. To trust the results of a qualitative study involving, say, ethnographic interviewing and/or participant observation, you similarly need assurance that the researchers were not simply “discovering” what they thought they already knew. But because such research requires in-depth involvement with the people being studied, the self-of-the-researcher becomes central to the gathering of data, requiring you to adopt different criteria for judging the reliability of the results (for excellent guidelines, see Lincoln & Guba, 1985, pp. 289–331). But what are you to make of an approach to research where the researcher serves not only as a data-gathering instrument, but also as the subject of the study? Autoethnographies, such as the following piece by Muriel Singer, intentionally “blur the boundaries between social science and literature” (Bochner & Ellis, 2002, p. 1). As subjective accounts, they doubly refract the social world, relying on both the experience and perspicacity of the researcher to inform and engage the reader. This requires yet another shift in how you critically evaluate them. When you read an autoethnography, at least one as gripping and emotionally complex as “Dry Salvages,” you get caught up in the unfolding of the narrative. You thus cannot objectively assess reliability, checking the degree to which the work satisfies a set of objective criteria. Because an autoethnography is a personal narrative, it demands from you a personal response. Does it move you? Enthrall you? Challenge your assumptions and open you to new understandings? What do you make of the researcher’s emotional and narrative integrity? Ask these sorts of questions as you enter and move through Muriel’s insider account of 9/11, and you’ll find yourself appreciating fresh possibilities for assessing
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.280 | 0.135 |
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".