Bibliographic record
Abstract
The future of work has long been an important topic of academic and managerial interest as well as of policy making. Yet, we still know little about how people who do the work consider its future. It is important to study this issue especially when the current situation is disrupted because imagining the future becomes especially crucial yet more challenging. This study asks how people imagine the future of their work when their present is deeply disrupted. It builds upon scholarship on the future of work and the role of digital technologies in bringing it about as well as on imagined futures. It examines longitudinally the case of an online community whose participants reacted to the COVID-19 pandemic that upended their current work situation and made their future more uncertain. This study contributes to scholarship on the future of work by highlighting the importance of people’s considerations not only of the future, but also of the ways in which their imagined future is connected to their current situation. It also adds to scholarship on online communities by revealing them as settings where people get to confront their current situation and collectively generate imaginations of the future.
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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.048 |
| Scholarly communication | 0.022 | 0.038 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".