Partnership work as practice of and preparation for navigating complexity, uncertainty, and precarity
Bibliographic record
Abstract
This iteration of Voices from the Field explores the numerous ways that faculty, staff, and students see partnership work as both a practice of and a preparation for navigating an increasingly complex, uncertain, and precarious world.Sustaining its commitment to offering a venue for a wide range of contributors to address important questions of students-as-partners work without going through the intensive submission, peer-review, and revision processes, this section of the journal offers glimpses into how those in higher education already enact or call for students-as-partners work that both constitutes and contributes to building capacity, resilience, and more connected and sustainable ways of being in the present and in the future.The invitation we extended for this iteration of Voices from the Field was to consider UNESCO's (2020) assertion that we live in a world of "increasing complexity, uncertainty and precarity" characterized by "persistent inequalities, social fragmentation and political extremism" (p.11).The specific prompt we offered was: "In what ways does students-as-partners work develop capacity to work under conditions of-and prepare for-complexity, uncertainty, and precarity?"We received responses from 32 contributors.These included 16 faculty members, eight staff or administrators, and seven students from 18 universities in the UK, four universities in Canada, two universities in the US, and one each from Australia and Pakistan.These contributors occupy a range of roles in higher education: undergraduate, master's, and Ph.D. students; faculty in disciplines from nursing through theater; staff in various campus offices; and administrative leaders.Across all contributions we found adamant assertions regarding the potential of studentsas-partners work as both a practice of and preparation for navigating an increasingly complex, uncertain, and precarious world.Some contributors reflected on what needs to be unlearned to embrace such an orientation.Others offered examples of institutional efforts and individual
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 | 0.080 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.113 |
| Scholarly communication | 0.035 | 0.038 |
| Open science | 0.004 | 0.047 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".