To Level the Playing Field, Develop Interest
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
Individuals do not all come to tasks, activities, or assignments with the same readiness to engage. Differences in the ability to focus, comprehend, or problem-solve can lead to inequalities of outcome and make learners less likely to realize their potential. Given that interest development supports persistence, conscientiousness, and the ability to work with negative feedback, educators and policymakers could help to increase educational opportunity for all by promoting the development of interest. Interest is a cognitive and motivational variable that describes (a) engagement, or participation, with some content (such as physics, writing, or baseball) and also (b) the motivation to continue to seek opportunities to engage with that content: seeking information, posing questions, and tackling challenge. Interest works because the information search it creates is rewarding. The development of interest heightens understanding and sustains engaged work. It also positively influences outcomes such as performance and continued enrollment. Even the development of a little interest can make a difference. Educators and policymakers can enhance educational opportunities by promoting interest development. Methods are described.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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