Bibliographic record
Abstract
The challengeOur challenge was to offer students something screen-free to support their academic and personal wellbeing and to utilise their self-reliance.We were driven to help in some wayvia curriculum, resources, and strategiesduring the prolonged Covid-19 pandemic and its associated isolation, loneliness, digital dependence, and fragmentation.We felt compelled to explore how thriving at the heart of our professional programmes might still be possible, even if only in micro-moments, given the Covid-19 context.We wondered what we could suggest that students could turn to independent of time zones, literal bandwidth, and space constraints.This led us to incorporate inner-resourcing strategies into our nationally recognised programme, Thriving in Action (TiA).TiA began just over four years ago at X/Ryerson University in Toronto, Canada, where it has so far supported more than 1,000 students and spawned a community of practice involving colleagues in 35 Canadian post-secondary institutions.In its original form, TiA welcomed students from all years and programmes who self-identified as academically or personally struggling into a semester-long cohort to learn Positive Psychology essentials, like selfcompassion and gratitude, intertwined with integrative learning strategies.While many core aspects remain, what has changed is an enhanced commitment to offer students ever-ready, somatic approaches to well-regulate, refocus, and re-embody.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.044 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".