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Record W4400503601 · doi:10.54337/nlc.v14i1.8098

Navigating Knowledge

2024· article· en· W4400503601 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the International Conference on Networked Learning · 2024
Typearticle
Languageen
FieldPsychology
TopicCognitive and psychological constructs research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

In this workshop, we will demonstrate four research tools: 1) card sorting, 2) upward laddering, 3) think-aloud, and 4) a search visualizer. Card sorting can be useful as an exploratory knowledge modelling method to gain insight into people’s understanding of their surrounding world. Derived from Kelly’s Personal Construct Theory (PCT) (Kelly, 1955, Herd, 2001), card sorting can help to explore peoples’ constructs and their perceptions of how the constructs relate to each other; in other words, card sorting can help to elucidate mental frameworks. Although there are many kinds of card sorting, we will guide participants through a hands-on exercise using single-criterion card sorting (using both digital and in person techniques). Participants will learn about text-based, image-based, and object-based options, when to use card sorting, how to collect data, and how to analyse card sorting data through visual-numeric ‘heat maps’ (co-occurrence matrices). The card sorting exercise will lead into upward laddering, a technique that is often used to complement card sorting. While card sorting provides evidence of how constructs are related, upward laddering allows exploration into goals and values (Rugg & Gerrard, 2023). Workshop participants will have an opportunity to access a simple, robust, newly created online tool for upward laddering. Also used alongside card sorting is the think-aloud method which involves both observation to see how people perform a task and think-aloud to hear what people are thinking and noticing while they perform the task. This inexpensive, easy-to-use method allows researchers to tap into reasons and tacit knowledge. The fourth tool we will demonstrate is a newly launched search visualiser (SV). Using keywords, this search tool allows researchers to comb through specific databases and/or to access Google results. Rather than simply return a list of links to articles, the SV returns a visual depiction of the keywords within each text. Each key word is represented as a coloured square. A user can hover their mouse pointer over a given square to see the phrase within which the word appears. Using this tool, researchers can get a better, visual sense of whether the article is likely to offer useful content. There is now a version with audio for visually impaired users as well as a version that can explore synonyms to support textual-literary analysis.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.646
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.098
GPT teacher head0.426
Teacher spread0.328 · 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