Bibliographic record
Abstract
My intent here is to explore the relationship between the concepts of validity and reflexivity. Recognizing the long-standing debate over what constitutes validity (e.g., Quite contrary to other scholarly papers I have authored, I do not enter this endeavor with a specific thesis or argument; rather, in the spirit of complex thinking I attempt a "layering that bring(s) forth depth and creation of new meaning" (Doll, 2008, p. 74). My intent is to juxtapose representations of validity and reflexivity as having unambiguous meanings with other possible meanings and to see how this plays out. That is, rejecting the notion that these concepts represent a dichotomy, I explore the intertwined meanings from three paradigms significantly influencing my research (feminism, poststructuralist and positivism) to see what possibilities of meaning may emerge from this "third space" As I am a registered nurse, this exploration takes place in the context of healthcare research, where the stated focus of research is the patient and/or improving patient care. Currently, the majority of healthcare related decisions are grounded in valid quantitative evidence.
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 | 0.283 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.122 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| 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".