Bibliographic record
Abstract
Abstract This article explores the meaning and context of crowdsourcing at the municipal scale. In order to legitimately govern, local governments seek feedback and engagement from actors and bodies beyond the state. At the same time, crowdsourcing efforts are increasingly being adopted by entities – public and private – to digitally transform local services and processes. But how do we know what the “the right to the city” (RTTC) means when it comes to meaningful and participatory decision-making? And how do we know if participatory efforts called crowdsourcing—a practice articulated in a 2006 Wired article in the context of the tech sector—when policy ideas are sought at the municipal scale? Grounded in the ideals of Henri Lefebvre’s RTTC, the article brings together typologies of public participation to advance a conceptualization of ‘crowdsourcing’ specific to local governance. Applying this approach to a smart city initiative in Toronto, Canada, I argue that for crowdsourcing to be taken seriously as a means of inclusive and participatory decision-making that seeks to advance the RTTC, it must have connection to governance mechanisms that aim to integrate public perspectives into policy decisions. Where crowdsourcing is disconnected to decision-making processes, it is simply lip service, not meaningful participation.
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 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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.122 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".