Supporting Flexibility and Awareness in Localisation Workflows
Bibliographic record
Abstract
A key strategy for supporting users in distributed work systems is to help them maintain awareness of the state of the work system and of the work being done by others. At the same time, many knowledge intensive industries are embracing the technologies that have underpinned the Web 2.0 movement to allow open user generation, annotation and modification of content. These technologies can potentially provide a useful platform for supporting awareness and distributed teamwork. However, as distributed content generating activities become more valuable, organisations aim to optimise them, often by modelling and monitoring the workflows involved and augmenting them with software services. Currently, however, these two approaches do not integrate well and there is little system support that integrates the centralised monitoring and management of workflow with the open communications that is characteristic of web-based user content generation. In this paper we examine the use of both techniques in the localisation industry, and based on this analysis we propose a platform that combines the visibility and awareness support of open content generation between users with their involvement in a centrally managed workflow.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".