Bibliographic record
Abstract
This PDF includes the editorial and all the articles published in this Special Issue on (Forced) Migration and Media. This issue is the result of two workshops organised at the University of Leicester: a workshop on (Forced) Migration and Media-research that took place on the 13th of June 2016 and a Community Impact event that was organised on the 18th of July, 2016. These workshops were a response to the topical interest for refugees’ access to digital technology and the dehumanizing language used in, especially but not limited to British, media regarding migrants and/or refugees (Berry, Garcia-Blanco, & Moore, 2015). (Forced) was purposefully bracketed as the label ‘refugee’ has its own difficulties. The differentiation between economic and forced migrants for instance negates that reasons behind migration are often multi-causal and multi-layered. It reinforces thinking in dichotomies that homogenizes and tends to negate in-between complexities, as is often appropriated as a governing tool to victimize, exclude and curtail the rights of human beings (Crawley & Skleparis, 2017; Lindley, 2010; Zetter, 2007). In this editorial, we reflect upon the main outcomes of the workshop we and other PhD-colleagues organised on the 13th of June, 2016, and connect them to the articles within this Special Issue.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.070 | 0.021 |
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".