RECIPROCAL PLATFORM LABOUR IN THE NIGERIAN SOCIAL MEDIA VIDEO INDUSTRY
Bibliographic record
Abstract
This paper explores how content creators in the Nigerian social media video industry navigate the economic, infrastructural, and cultural logics of digital platforms through practices of reciprocal labour. As is the case in many global contexts, the economic formalization of social media platforms, such as YouTube, Facebook, and TikTok, have enabled the emergence of for-profit social media video production in Nigeria. This paper focuses on the under-studied intersection of platform logics and labour relations in this industry. Drawing on 10 semi-structured interviews with Nigerian content creators, combined with analysis of the domestic trade press, we observe that creators struggle to generate visibility in a highly saturated social media landscape. This visibility imperative is not unique to Nigeria. What sets Nigeria apart, however, is the local political economy of video production, which translates into high production costs, which are offset by orchestrating practices of informally organised reciprocal labour. Nigeria thus provides a relevant perspective to ongoing debates in platform research that seek more regional specificity and seek to decentre the Global North as their point of reference. To heed that call, the specific labour practices we highlight, those of reciprocal labour, reflect the broader informal economies and traditional kinship norms in Nigeria. Exploring this mode of work showcases the intersections among creative labour and cultural dynamics in a given national context vis-à-vis the unifying business models and centralized governance frameworks of platform companies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".