Bibliographic record
Abstract
The emergence of various digital platforms in the era of the internet technology revolution, in addition to giving birth to various new job types, also gives rise to a new term for workers, the term ‘mitra’ in the "gig economy" and gradually replacing previous terms such as buruh, karyawan, pegawai. This article attempts to decode the meaning of "mitra" using Norman Fairclough's critical discourse analysis (CDA) which studies the use of language (verbal and written) as a form of social practice. Through Fairclough's three CDA levels; Text (micro level) is seen as a representation of something containing a certain ideology; Discursive practice (meso level) refers to the production, distribution, and consumption of text; Socio-cultural practice (macro level) refers to the context outside of the text such as the context of society, culture, and politics that influence the existence of text, we can reflect the ideological effects that reproduce unequal power relations in the word "mitra" used by various digital platforms. The term "mitra" referring to the ideology of "platform capitalism" makes them not considered as "pekerja" or "buruh" and eliminating them as legal subjects protected by the Labor Law. The problem is also the new work relationship is still unclear in many aspects, which can lead to exploitation of "mitra" by the company.Keywords: critical discourse analysis, ilusi kemitraan, gig economy, platform capitalism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".