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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.154 | 0.068 |
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".