Bibliographic record
Abstract
The internalization and globalisation of higher education has led to new demands and social situations that require educational reforms because the existing structure is insufficient to fix the current issues. Analyzing current issues in pedagogy such as power in grading, I draw on the work on Vygotsky and Bandura to propose a possible start for future solutions. One of the main issues analysed in this paper is criticisms of the current focus on grading and individual characteristics such as agency for evaluating learning. I argue that this individualistic perspective may only keep the same problems in higher education instead of solving them and it is better to view the higher education as means for enhancing collective agency, solidarity, and reducing inequality. Employing epistemological caution, I am not saying that agency theory does not have merits, but that the space for action with human agency is limited by situational factors. One situational factor is understanding learning and teaching styles as well as how knowledge develops in classrooms so that learning can occur. Instructors are mediators in students’ learning because they have the power to motivate them to learn or disengage them from learning.
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 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 | 0.003 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".