Are Psychological Variables Relevant to Evaluating Geoinformatics Applications? The Case of Landmarks (Vision Paper)
Bibliographic record
Abstract
Interdisciplinary integration of spatial cognition and spatial computation promises to create better spatial technology based on findings from cognitive psychology experiments. Using the example of psychological studies and computational modelling of landmarks, this paper argues that core evaluation criteria of both disciplines are not well aligned with the goal of evaluating landmark-enhanced navigation support systems that support users in everyday wayfinding. The paper raises two points. First, it reviews evaluation criteria used in the interdisciplinary field of landmark research. It is argued that when to consider the role of landmark-enhanced navigation support systems in everyday life of their users, different evaluation criteria are needed. If strictly-psychological or strictly-computational criteria continue being prioritised by the community, we risk undervaluing significant technological contributions. Second, it proposes one such potential criterion: testing whether the cognitive task has changed due to equipping users with the new technology. This goal might be achieved at the expense of criteria typical to strictly-psychological studies (such as spatial memory of landmarks along the travelled route) or strictly-computational studies (such as efficiency and accuracy of a landmark-selection algorithm). Thus, promoting and implementing alternative evaluation criteria comes with methodological risks. In order to mitigate them we propose a process based on pre-registration of "postdiction" studies and hope to stimulate a further debate on a consensus-based approach in the community.
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 | 0.037 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".