Bibliographic record
Abstract
A n interesting phenomenon has begun to emerge in health care, one that wethink needs to be examined a little more closely. It’s a phenomenon that we refer to as the bastardization of research designs. The phenomenon is seen most often in qualitative designs. Some refer to the process as the development or improvement of research designs; others see it as poor science. The major concern here is with the use of a research design developed by anthropology and borrowed by health care disciplines, called ethnography. All research designs were developed through trial and error to arrive at answers (dependable answers that reflected truth or fact) to common questions being asked by scientists. The earliest of the designs, the one with the longest his-tory, is the experimental design. At its heart is the basic concept of “experimen-tation, ” or changing something to see what will happen, in which a variable is selected to be “manipulated ” or changed. If the variable is not manipulated or changed, we do not have an experimental design. It is as simple as that. All research designs have a part of the design and a research process that is the signature to the design. Over time, there have been variations to the design, with
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 | 0.042 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.011 | 0.067 |
| Scholarly communication | 0.022 | 0.054 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".