Bibliographic record
Abstract
Reading or writing online user-reviews of places like a restaurant or a hair salon is a common information practice. Through its Local Guides Platform, Google calls on users to add reviews of places directly to Google Maps, as well as edit store hours and report fake reviews. Based on a case study of the platform, this chapter examines the governance structures that delineate the role Local Guides play in regulating the Google Maps information ecosystem and how it frames useful information vs. bad information. We track how the Local Guides Platform constructs a community of insiders who make Google Maps better vs. the misinformation that the platform positions as an exterior threat infiltrating Google Maps universally beneficial global mapping project. Framing our analysis through Kuo and Marwick’s critique of the dominant misinformation paradigm, one often based on hegemonic ideals of truth and authenticity. We argue that review and moderation practices on Local Guides further standardize constructions of misinformation as the product of a small group of outlier bad actors in an otherwise convivial information ecosystem. Instead, we consider how the platform’s governance of crowdsourced moderation, paired with Google Maps’ project of creating a single, universal map, helps to homogenize narratives of space that then further normalize the limited scope of Google’s misinformation paradigm.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".