Bibliographic record
Abstract
“...fear founded on mere possibility is less helpful than wariness grounded in understanding.” (Monmonier 2002: 2) Emergent technologies which manifest and are popularized by surveillance practices are often promoted in ways that betray biases, conspiracy, or determinism. These approaches do nothing to further an academic examination of such innovations and as such, only serve to perpetuate fear. There is an undeniable technological trend towards digitization and cartography is no exception: Google Street View illustrates this change. Predicting the success or failure of particular products and trends is irrelevant. However, tracking the sociological progress of these technologies permits an invaluable insight into the workings of our social world. These technologies alter our understanding of maps, changing the conditions of our experience from static knowledge to electronic dynamism. In my examination of the mapping tool, I attempt to deconstruct popular (mis)conceptions/perceptions surrounding the application, arguing that the media habitually approaches the application with a lens that either trivializes or sensationalizes its properties and usages. As a corollary of this, mass media tends to provide blanket coverage on fashionable topics, while simultaneously avoiding the examination of potentially more questionable social and political implications.
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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.023 |
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".