Types and categories of personal projects: a revelatory means of understanding human occupation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it