OOPS, Turning MIT Opencourseware into Chinese: An analysis of a community of practice of global translators
Bibliographic record
Abstract
An all-volunteer organization called the Opensource Opencourseware Prototype System (OOPS), headquartered in Taiwan, was initially designed to translate open source materials from MIT OpenCourseWare (OCW) site into Chinese. Given the recent plethora of open educational resources (OER), such as the OCW, the growing use of such resources by the world community, and the emergence of online global education communities to localize resources such as the OOPS, a key goal of this research was to understand how the OOPS members negotiate meanings and form a collective identity in this cross-continent online community. To help with our explorations and analyses within the OOPS translation community, several core principles from Etienne Wenger’s concept of Communities of Practice (COP) guided our analyses, including mutual engagement, joint enterprise, shared repertoire, reification, and overall identity of the community. In this paper, we detail how each of these key components was uniquely manifested within the OOPS. Three issues appeared central to the emergence, success, and challenges of the community such as OOPS: 1) strong, stable, and fairly democratic leadership; 2) participation incentives; and 3) online storytelling or opportunities to share one’s translation successes, struggles, and advice within an asynchronous discussion forum. While an extremely high level of enthusiasm among the OOPS members underpinned the success of the OOPS, discussion continues on issues related to quality control, purpose and scope, and forms of legitimate participation. This study, therefore, provides an initial window into the emergence and functioning of an online global education COP in the OER movement. Future research directions related to online global educational communities are discussed.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".