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Enhancing spatial learning and mobility training of visually impaired people—a technical paper on the Internet‐based tactile and audio‐tactile mapping

2003· article· en· W2025224679 on OpenAlexaffvenueabout
Eva Siekierska, Richard Labelle, Louis Brunet, Bill Mccurdy, Peter Pulsifer, Monika Rieger, Linda O'Neil

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2003
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsGolder Associates (Canada)University of CalgaryNatural Resources Canada
Fundersnot available
KeywordsOrientation and MobilityGeospatial analysisComputer scienceThe InternetVisually impairedMultimediaHuman–computer interactionCartographyWorld Wide WebGeography

Abstract

fetched live from OpenAlex

Thanks to advances in technology such as Internet‐based mapping and the development of special inks, papers and global positioning systems, map making for vision impaired people can provide a range of products (e.g., tactile, haptic and audio‐tactile maps) that can improve their independence, self‐confidence and everyday life. The applications of tactile maps include learning spatial concepts and geography; audio‐tactile maps combined with access to geospatial information can enhance mobility and independence. In 1998, the Mapping Services Branch (MSB) of the Earth Sciences Sector of Natural Resources Canada initiated a tactile mapping program. Now referred to as the Government on Line—Mapping for the Visually Impaired Project (Natural Resources Canada 2003a), it aims to serve the community with special needs, with emphasis on those who are blind or visually impaired, their teachers and mobility instructors. This technical paper discusses the various types of tactile and audio‐tactile maps of Canada developed by the MSB in cooperation with its partners and describe the current research and development activities carried out within the project. It also leads the reader to further information on audio‐tactile maps and touch‐ and sound‐based computer interfaces.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.227
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2003
Admission routes3
Has abstractyes

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