Bibliographic record
Abstract
This special issue marks the twenty-fifth anniversary of the publication of J.B. Harley's “Deconstructing the Map” (1989), which has had a major influence in the fields of critical cartography, the history of cartography, and human geography more generally. Over the last quarter century, this essay and related works have also been widely cited by scholars from a broad range of disciplines across the social sciences and humanities, serving as a key reference for those seeking to theorize the spatial politics of maps and mapping. Through such citational practices, “Deconstructing the Map” has acquired a canonical status as one of the classics of critical cartographic theory, yet the limitations of its theoretical and methodological analyses are widely acknowledged even by Harley's strongest supporters. The contributors to this special issue discuss their own critical engagements with this foundational text as well as the extent to which Harley's work still resonates with contemporary perspectives in the field of critical cartography today. The broader aim of this collection is therefore not to further canonize Harley as the patron saint of critical cartography but rather to think through the limits of “Deconstructing the Map” to ensure that current and future theorizations of the power of mapping remain open to self-critique and new becomings.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".