Analysis of the Decline in Interest Towards School Science and Technology from Grades 5 Through 11
Bibliographic record
Abstract
Interest in school science and technology (S&T) remains an important issue as it is linked to achievement and the intention to pursue studies or careers in S&T. Around the world, a number of studies have shown that interest in S&T declines with school years. However, some divergences from the general trend have been demonstrated in certain contexts, sub-periods, or for closely related subconstructs. We administered 2,628 questionnaires to students in grades 5 through 11 in the province of Québec, Canada. The questionnaire explored many factors (including out-of-school and school-related preferences, difficulty, importance, frequency), allowing us to track these closely related variables for a seven-year period. Among others, the results show a general degradation in-school S&T factors but an improvement in out-of-school S&T variables and of interest in S&T studies and careers. S&T is perceived as increasingly difficult and valuable compared with all other subject matters taken one-on-one. Some shorter fluctuations are analysed and interpreted in comparison with the evolution of certain teaching practices.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".