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Record W2334738078

Creating the Blackfoot digital library: the challenge of cultural sensitivity.

2009· article· en· W2334738078 on OpenAlexaboutno aff
Marinus Swanepoel

Bibliographic record

VenueOpen ULeth Scholarship (OPUS) (University of Lethbridge) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsDigital librarySensitivity (control systems)Computer scienceArtEngineeringLiterature
DOInot available

Abstract

fetched live from OpenAlex

In the mid 1990’s it was estimated that there are only about 5,000 – 8,000 speakers of the 
\nBlackfoot language and that the numbers were declining. The decline of the language was 
\nexacerbated by the absence of a generally accepted writing system. The orthography most 
\ncommonly used for writing Blackfoot on the three Southern Alberta reserves was only approved as the official writing system in 1975. 
\nThis resulted in very little written material being produced by the Blackfoot people that captured their history In 2006 the University of Lethbridge and Red Crow Community College joined forces in to ensure 
\nthat as much as possible of the Blackfoot cultural record will be preserved and made accessible 
\nthrough the creation of a Blackfoot Digital Library. 
\nA foundational requirement of the digital library was cultural sensitivity and specifically that it must appropriately honor the Blackfoot worldview. In the traditional Blackfoot worldview the underlying premise is that all knowledge is derived from place, which posed a significant challenge. The solution was the design of a custom-made search interface that displays the search results on a 
\ndigital map where the user is immediately confronted with the land. The map, displays the 
\nplace(s) where the assets that were retrieved by the search, originated from and offer access to the various assets themselves. 
\nThe presentation informs on the challenges faced by the initiative.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.010
Open science0.0020.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.289
Teacher spread0.242 · 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 designTheoretical or conceptual
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

Citations0
Published2009
Admission routes1
Has abstractyes

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