Bibliographic record
Abstract
Grow Your Academic Resilience is interactive workshop aimed at equipping students with practical tools to nurture their academic resilience, or their ability to deal with academic challenges and setbacks (Martin and Marsh, 2008). The session helps students recognise the qualities of a growth as opposed to fixed mindset (Dweck, 2006), and supports them to feel confident in dealing constructively with feedback. Students are encouraged to identify strengths they possess and consider the skills they need to achieve their academic goals. Research demonstrates that resilience is an attribute that positively impacts student wellbeing, engagement, and academic achievement (Turner, Scott-Young and Holdsworth, 2017). Consequently, we believe universities play a key role in developing the resilience of students, therefore introducing students to this concept at the earliest opportunity is paramount. Feedback to date has been positive and we aim to grow the number of sessions we deliver. Our objective was to deliver an adapted session and elicit feedback from our peers for future development. Participants took part in a 45-minute workshop as university students. Alongside this, commentary was provided discussing the nature of the activities. Finally, participants were given 15 minutes to share experiences and offer constructive suggestions. Resources were shared, alongside presentation notes. Session Plan: Fixed vs. Growth Mindset quiz Grow your academic resilience (bespoke worksheet) Your feedback plan The session addresses the following Learning Outcomes: Understanding what it means to be academically resilient Recognising a growth Mindset Discovering practical tools to nurture your resilience Dealing confidently with feedback
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.033 |
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".