Bibliographic record
Abstract
In this book, we have highlighted the process of conducting a qualitative research project in physical culture. It is clear that each research project needs a clear design, it has to be well done and well written to provide meaningful knowledge. We introduced the 7Ps approach as a tool that can help in the process of conducting quality qualitative research. Following this approach, the researcher should first be able to identify a clear purpose that then helps to locate the project within an appropriate paradigm. The researcher can then embark on a process of selecting appropriate qualitative research practices (methods) and ways (politics) of interpreting the acquired empirical material. After an analysis, the researchers should choose the appropriate way of (re)presenting the results and be able to explain how they have ensured that the project is of the best quality it can be (the promise). It is clear, however, that the researchers’ paradigmatic stance determines the choices embedded in the many of the other Ps. Therefore, to summarise the main tenets of our book, we provide the following table (Table C1) that further clarifies each of Ps and their relationship to the researchers’ choice of paradigm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".