Exploring Possible Ramifications of Human Directional Deficiency in Computer Science
Bibliographic record
Abstract
Cognitive scientists, psychologists, and other researchers have endeavored over the past three decades to identify the cognitive functions underpinning human navigation and its possible correlations to other characteristics. The answer to the basic question of how/why some people are good at finding directions and some people are not, is yet to be determined conclusively. It has been reported that a certain percentage of people in the United States and Canada (as the target audience) suffer from what is variously referred to as directional deficiency, direction dyslexia, direction dysfunction, geographical dyslexia, human homing deficiency, or geographic insensitivity. Part of the objective of this thesis work was to investigate the ramifications of this deficiency, to explore what this deficiency may correlate with (with a special focus on spatial cognitive skills, programming, and debugging), and to suggest ways of detecting this deficiency. The scope of the thesis work included both theoretical and empirical studies of human direction sensitivity and the cognitive tests that attempt to test hypotheses about individual differences in spatial/temporal attention spans as well as a set of program comprehension questionnaire-based tests about the debugging/testing of computer programs and program comprehension. This was done in the context of the relevant cognitive-based perceptual and spatial tests. The tests results obtained suggest that the programmers' directional detection skills might have some correlations with their program comprehension abilities.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".