Here be dragons: Exploring Cartography in Educational Theory and Research
Bibliographic record
Abstract
In the literature on complexity theory it has been noted that the increasing interdependence, non-linearity, and adaptiveness of social and other systems require forms of representation that can accommodate such complexity. In this essay I argue for examining the possibilities of cartography (mapmaking) in and of educational theory and research. Cartography offers alternative forms of representation that are better suited to capturing complexity. The performativity of cartographic representations, moreover, produces different knowledge. I present four features of educational theory, research, and practice that suggest the relevance of cartography. The first is the widespread use of narrative models of representation and interpretation. Narrative discourse typically emphasizes temporality; maps are an alternative or complementary discourse that visualize and help to examine the spatial character of educational experience. The second feature is that spatial metaphors abound in educational discourse, including the recently ubiquitous metaphor of the web or network. Cartographic discourse is well suited for representing, interpreting, and critiquing these metaphors. The third feature is the increased use of hyperlinked information in educational theory and practice. Maps are better suited to capture and to enable the questioning of the rhizomatic interconnections of hypertextual reading and writing practices than more linearly organized discourse. The fourth feature of education is that it is a social institution that plays a central role in the social positioning of subjects. When the discursive and physical mechanisms through which students and teachers are separated, categorized, ranked, and assessed are cartographically represented and analyzed, new questions can emerge about these mechanisms of power.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".