Bibliographic record
Abstract
Disciplines anchor themselves using a particular kind of epistemology and ontology to produced it’s knowledge. In healthcare, quantitative research has been dominant were in social sciences, qualitative research was. However, in the last two decades, we have seen a mixing (better said in French with the word métissage) of strategies within those two disciplines. Despite the newfound acceptance of qualitative research within the healthcare field, some criticism about those strategies still exists and still impact the feasibility of conducting qualitative research, especially in hospital setting. More particularly, when it comes to the systematisation of the method, when conducting ethnography. In this paper, I argue that ethnography does remain scientifically rigorous, especially when it is informed by theory and used consistently. This article presents the ways in which I negotiated the uncertainties of doing a hospital ethnography on the use of the ventilator by using concepts from Latour’s (2005) Actor’s Network Theory, of ‘mediators’ and ‘intermediaries’. Staying attuned to various actors in the healthcare setting and taking care to ensure that whatever my research brought into the field maintained an intermediary status enabled me to alter my methods in the field while still respecting the necessity to gather data systematically.
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 | 0.083 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".