Collaboration Through a Computer Screen: Migrant Integration Services and the Challenges of Co‐Producing Services Online
Bibliographic record
Abstract
ABSTRACT In this article, I explore the impact of shifting from in‐person to online service delivery on individual and group modes of co‐production of migrant integration services during the COVID‐19 pandemic. The study adds to the literature concerning the effects of digital co‐production on social services that rely heavily on relational interactions to build trust. Using qualitative methods, the research explores how digitalisation affects four key elements: interaction, motivation and commitment, resources and skills, and empowerment and inclusion. While digital co‐production introduces new modes of interaction that may enhance inclusion in some types of individual co‐production, it poses significant challenges for all elements of group co‐production. The study concludes that while digital co‐production may offer some benefits, its application to highly relational services with vulnerable populations is often detrimental, questioning its desirability and effectiveness in such contexts.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".