Bibliographic record
Abstract
Abstract This hybrid edited collection embodies the experience of living in liminal spaces, containing as it does between its covers, both academic essays grounded in critical inquiry as well story-chapters that employ a variety of creative writing genres. With potential to appeal to an academic reader and scholar of migration, its objective is also to be equally accessible to a more mainstream readership, particularly if they have a general interest in migration stories and issues. In addition to exploring some of the key trajectories and tropes that pertain to the migration experience, the volume also goes behind the scenes to tell the story of the workshopping process that the StOries Project journey was for its participants. One of its objectives is to show the reader how the project led to the birthing of the twenty creative writing chapters this volume contains. This edited volume then, emerged specifically from a fusion of scholarly inquiry and creative writing that participants (also its contributing authors) engaged with. In this first chapter, several key contexts, themes, and approaches that pertain to migration are discussed; significant related concepts are organised by the editors in thematic categories. Finally, in this introductory chapter, we also discuss recent developments in the respective migration trajectories, including identifying gaps in migration theory and methodology that we feel creative writing can contribute towards addressing. The significance of amplifying personal voices and unique stories is reiterated through sharing examples from the 20 stories collected in this volume.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".