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Record W4406634719 · doi:10.69598/artssu.2025.3142.

Understand and Find a Mechanism to Enhance the Power of App-based Food Delivery Riders in Thailand

2025· article· en· W4406634719 on OpenAlexaff
Nakarin Charoenloasiri, Narakate Yimsook, Christine M. Walsh

Bibliographic record

VenueJournal of Arts and Thai Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMechanism (biology)Food deliveryBusinessPower (physics)Internet privacyMarketingAdvertisingComputer sciencePhysics

Abstract

fetched live from OpenAlex

Background and Objectives: To explore new possibilities for finding spaces that can support the opportunity for dialogue and exchange among food delivery workers on digital platforms, which will strengthen their collective efforts to demand rights for the group, instead of establishing a union that is not recognized by law. This is because food delivery workers are currently controlled by digital technology rather than working in traditional physical workplaces. In the case of Thailand, food delivery workers are not classified as employees with social protection rights and do not have the legal right to form a union or demand rights under labour laws. As a result, their voices are ignored, and they lack bargaining power to demand better working conditions. Methods: The study employed a method of reviewing relevant academic documents, gathering data on the environment related to app-based food delivery workers, and synthesizing this information to explore the possibilities based on the actual conditions experienced by individuals in this occupation. Results: Although digital platforms disrupt the working conditions of food delivery workers, they can also serve as new public spaces for fostering community and advocacy. These online spaces enable workers to share experiences, discuss challenges, and collectively push for better working conditions and labour rights. By facilitating open dialogue and mutual support, these platforms can strengthen their collective voice, empowering workers to advocate for changes in their work environment. Application of this study: Food delivery workers can increase their bargaining power for labour rights by utilizing online spaces, as these can be free spaces from hierarchical legal structures. This can lead to demands arising from the consensus of the food delivery workers in the online system, empowering their collective voice to negotiate and protect their labour rights with authorities. Conclusions: Establishing an online platform as a communicative space for food delivery riders is crucial for addressing the challenges they face, particularly their lack of bargaining power. This digital public space will serve as a vital forum for riders to share experiences, discuss common issues, and collectively determine goals, thereby fostering a sense of community and solidarity. By facilitating open dialogue and mutual understanding, such a platform empowers riders to advocate for improved working conditions and more labour rights. Ultimately, this participatory mechanism not only enhances their ability to negotiate with agencies but also strengthens their collective voice, promoting a sustainable and supportive work environment.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.115

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.290
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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