Types and categories of personal projects: a revelatory means of understanding human occupation
Bibliographic record
Abstract
Choice of activity and the way it is described may have little to do with the presence of disease and may or may not align with predetermined conceptual or practice frameworks. The present study examines data previously collected by use of Personal Projects Analysis (PPA) in order to compare the types of projects listed by people with and without multiple sclerosis and to compare the categories of projects selected by both groups to those pre-established in the literature. Secondary analysis tests the differences and similarities in the types of personal projects between two groups, multiple sclerosis (n = 38) and control group (n = 25), matched for demographic characteristics. The analysis compares the categories of personal projects generated by people in both cohorts to pre-established frameworks. No significant difference was found between the types of personal projects chosen by the two cohorts. For 57.2% of participants the self-generated categories matched those from the literature, whereas it diverged for 18.2% of the categories of personal projects generated by participants. The study demonstrates that people with and without multiple sclerosis engage in activities that are similar despite the presence of multiple sclerosis, and that category systems should be used cautiously.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".