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Record W2740825413 · doi:10.20361/g23q2k

Gallery Interactives: From Grizzlies to Polar Bears by Canadian Museum of Nature

2017· article· en· W2740825413 on OpenAlexvenueaboutno aff
Ellen Norlander

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2017
Typearticle
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsInteractivityComputer scienceSimple (philosophy)Focus (optics)Matching (statistics)Adaptation (eye)DanceHuman–computer interactionVisual artsMultimediaPsychologyArtMathematicsEpistemology

Abstract

fetched live from OpenAlex

“Gallery Interactives: From Grizzlies to Polar Bears”. Canadian Museum of Nature, 16 Dec. 2009, http://nature.ca/discover/exm/frmgrzzlstplrbrs/index_e.html. Accessed 14 Feb. 2017.This short, educational game is designed to teach children about both polar and grizzly bears’ adaptations. It uses matching to allow users to choose between two different versions of an adaptation and drag them to either the polar or grizzly bear side. With the correct answer, a picture demonstrating that adaptation will appear and a box will pop up that explains in more detail the different adaptations. This game is educational in that it provides detailed information explained in simple language. It is also clear the user must drag the answer to a side, and what buttons to press once the box of information comes up. It holds the user’s hand almost too much but does not disrupt interaction. The interactivity of the game is limited because the only decisions users can make are by choosing the answers and if the wrong side is chosen, it moves back to the center. The graphic design is simple, using a muted blue color scheme, simple shapes, and a few static images but it could have used brighter colours or videos with audio to bring the animals to life. There also does not seem to be anything inventive because it is simply a matching game, with only two choices for each of the five levels, three of which focus on appearance. By having some incentive for the user to learn about each of the bears such as a mission or a problem to be solved, or by having more intuitive ways of choosing the answers, it would allow users to demonstrate their perceptiveness, build confidence and give them a feeling of investment in the material. For example, they could learn about the claws/pads by examining a trail of bear tracks. Overall this game is sufficient in its educational purpose but could do more to engage users.Recommended with reservations: 2 stars out of 4 Reviewer: Ellen NorlanderEllen Norlander is currently an MLIS student at the University of Alberta and hopes to enter the fields of either health sciences or academic librarianship. Her interests are reading anything and everything, playing piano, and blogging.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.000
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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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