Identity Alignment and the Sociotechnical Reconfigurations of Emotional Labor in Transnational Gig-education Platforms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Teaching has often been characterized as a “labor of love.” Despite their passion, teachers often find themselves underpaid and unrecognized, leading them to engage in taxing emotional labor. Emotional labor in traditional educational settings is not new. However, teaching as online gig work has become increasingly data-driven and transnational. With the burgeoning popularity of online educational industries in China, U.S. teachers are entering the transitional gig economy to teach students, parents, and educational standards in cross-cultural contexts. Based on 24 semi-structured interviews with U.S. teachers who worked on Chinese gig-education platforms, this paper documents their challenges and how such platforms reconfigure their emotional labor, enabling them to reaffirm their identities as teachers and caregivers and rekindle the passion that gave their lives purpose and meaning. However, these platforms, underpinned by Chinese cultural values and data-driven technologies (e.g., datafication, algorithms, and surveillance) — which we dub transnational emotional computing — demand emergent forms of emotional labor with which participants must contend. This work contributes to a human-centered conceptualization of identity alignment and carries theoretical and design implications for the future of transnational gig platforms, especially for cross-cultural digital knowledge labor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it