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Record W2319833901 · doi:10.1177/1555343414555159

Finding Common Ground

2014· article· en· W2319833901 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

VenueJournal of Cognitive Engineering and Decision Making · 2014
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCommon groundConstruct (python library)Computer scienceInterpretation (philosophy)Process (computing)CognitionTask (project management)Field (mathematics)Work (physics)Management scienceCognitive scienceEngineering ethicsPsychologySocial psychologyEngineering

Abstract

fetched live from OpenAlex

The article “Situation Awareness Misconceptions and Misunderstandings” (Endsley, 2015) discusses common fallacies noted in the literature in the interpretation of Endsley’s 1995 model of situation awareness (SA; Endsley, 1995). The clarifications presented in the article provide a more complete and comprehensible explanation of the model. As in the complex domains that we study, our attention suffers from limitations as we simplify each other’s work in our eager and well-intentioned quest to attack new and exciting problems. Dr. Endsley is to be thanked for giving us pause to rebuild our own SA of SA in her thoughtful article. SA has, for many years, been a powerful and influential construct. In our own work in cognitive work analysis (CWA), we have viewed SA as a complementary framework that challenges and drives CWA. Without doubt, the output of a CWA-based design process should be the design of a system that promotes better SA and performance (Burns et al., 2008). CWA and goaldirected task analysis may organize the world in slightly different dimensions, but the overall intent is the same: to create systems that support human decision making as well as we can. Indeed, it is this common intent that unites us in our field. To advance cognitive engineering, there are times when we must challenge each other, critique each other’s models, hunt for flaws, and identify promising new directions. Assuredly, this helps us progress, strengthen our methods, and deepen our understanding. This article clearly identifies such activity and responds to it. We are all better for this exercise, as it challenges both SA and all our approaches to grow and deepen. Acknowledging this, we would like to change our perspective to a larger one and discuss challenges facing cognitive engineering as a whole. In these challenges, our existing methods, SA, CWA, and other approaches must adapt and grow. Advancements in intelligent systems and automation have increased in the amount of data produced by information systems and yet placed the human in new roles. In many cases, these roles are partially in the loop and partially out of the loop and may involve supervisory control or may have the human working in systems with very little supervisory control at all because the automation is largely nontransparent. We outline three core areas of challenges: self-awareness and self-regulation, memory failures or performance with incorrect SA, and design for unstructured environments.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.028
GPT teacher head0.374
Teacher spread0.346 · 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