Bibliographic record
Abstract
Adaptation to change is not an easy process and sometimes does not happen at all. When people perceive that theirfreedom is going to be altered due to an unwanted change, they outwardly exhibit some symptomatic reactivebehaviors such as inertia, resistance, skepticism, and aggression. No matter how intense people’s reactance is, only afew of them may manage to examine the unwanted change more deeply and find a way to conform or adapt. Knowingthis, the current article focuses on a theoretical proactive model or a solution. The model mainly works on the idea ofrecognizing the symptomatic behavioral reactance of learners. In other words, in the face of the reactance-inducedbehaviors depicted in the model, the instructors can apply four proactive strategies of brainstorming, open transparentconversation, small scale project assignment and triple “c” rule by means of which they can walk learners safelytowards mutual trust, classroom stability and learner commitment. In the end, as the model is new, there is still enoughroom for further experimental researches on different aspects of the model in actual classroom settings.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".