Globalizing Flexible Work in Universities: Socio-technical dilemmas in internationalizing education
Bibliographic record
Abstract
We engage with and respond to the debate raised by this theme issue of the International Review of Research in Open and Distance Learning with a particular question in mind: namely, as universities are using new labor displacing technologies to export degrees to meet the international demand for higher education, how is this influencing – negatively and positively – the workers involved? Contemporary transitions in political and economic globalization are being used to press universities into becoming ‘transnational businesses,’ seemingly driven by a primary concern for marketing educational commodities. The neo-liberal politics driving these currents in universities are increasing the multiple online and offline networks. These local/ global meshworks engage the labors of a small but growing percentage of the world’s population (Singh, 2002, pp. 217-230). Writing this paper at Jilin University in China, we find that many of our academic colleagues and students have limited access to a personal desktop computer, the Internet, and email. They must pay for timed access to their email accounts and for downloading attachments. They do not have access to high-speed data networks. A timer indicates how long it will take to open and send emails. Around us, construction workers are building massive facilities to house the burgeoning on-campus student population. Their offline education is being supplemented – but not replaced by ever-advancing online technologies.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".