‘The bike breaks down. What are they going to do?’ Actor-networks and the Bicycles for Development movement
Bibliographic record
Abstract
This article reports on an empirical study of ‘Bicycles for Development’ (BFD) – a nascent movement whereby used bicycles are collected (often in the global North) and distributed in development contexts (often in the global South) with the aim of achieving a range of positive social outcomes (e.g. access to education). Drawing from interviews ( n = 32) with participants from 19 BFD organizations, and informed by actor-network theory (ANT), the analysis presented herein specifically highlights three key factors that facilitate and/or hinder BFD work: (a) government regulations that potentially stem or ease the flow of bicycles into development contexts; (b) the bicycle’s material constitution, and specifically its sturdiness (or lack thereof); and (c) environmental conditions that impact how bicycles are made, distributed and used. A key theme that cuts across these findings is the potential for non-humans to cause ‘frictions’ that potentially disrupt, divert, but also help in realizing the programmes of action of BFD organizations. We consider the relevance of these findings for both the BFD movement in particular and the wider Sport for Development and Peace (SDP) movement in general. Based on study findings, we argue for ANT as a useful framework for achieving a widened analytical focus and thus for delivering more robust accounts of development contexts under study.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".