Metadiscourse and the Gamification of Ride-hailing in the Platform Economy
Bibliographic record
Abstract
The advent of the sharing, gig or platform economy, in particular the spread of ride-hailing firms such as Uber and Lyft, has given rise to new forms of digital communication. According to the drivers, the use of sign-up bonuses, ratings, promotions, competitions and non-monetary rewards is intended to provide incentives to work longer and longer hours while Uber and Lyft (the main rival to Uber in the US and Canada) progressively cut pay rates, with the online discourse intended to manage relations with the drivers primarily for the benefit of the platform. The result is what has been characterised as the “gamification” of ride-hailing, with the terminology of hiring, employment contracts and wages being displaced by the discourse of video game techniques, graphics and non-cash rewards. Research into metadiscourse has so far focused predominantly on academic discourse, whereas the present study, based on insights provided by Mauranen (1993), Hyland (2005, 2017), Ädel (2006) and Ädel and Mauranen (2010) is intended to examine the ride-hailing discourse in terms of the use of metadiscourse devices such as hedges, boosters, attitude markers, engagement markers and self-mention, casting light on their pragmatic functions. Although in methodological terms exchanges between the platform and the drivers constitute an occluded genre, not in the public domain, some insights into this discourse can be obtained indirectly from driver critiques of working conditions.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".