Evaluation of Bilingual Sign Layout and Information Load before Implementation of New Signing System
Bibliographic record
Abstract
Before implementation of a new Parks Canada signing system, a laboratory study was carried out to compare bilingual sign layout options and to assess the relative information load of various sign elements. Participants were given a name or activity destination and shown a sign embedded in a road scene for a brief interval. Participants were asked to recall whether various elements were present on the sign. The key results on sign layout were as follows: on signs with side-by-side arrangement of languages, the primary language should be on the left; horizontal separators between destinations improve performance; vertically stacked arrangement of languages performs better than conventional side-by-side arrangement; placing all arrows on the left side of the sign as opposed to left and forward arrows to the left and right arrows to the right improves performance. Key findings regarding maximum information load were as follows: single-destination name signs performed much better than two- and three-destination signs for layouts with direction arrows, destination names, and activity pictograms. Two-destination signs performed better than three-destination signs for layouts with direction arrows, destination names, and distances. Regarding relative information load of sign elements, a name pair should be considered to have the same information load as a single activity pictogram, and a distance element has less information load than an activity pictogram.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".