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Record W2065859023 · doi:10.1145/1520340.1520349

Dying, death, and mortality

2009· article· en· W2065859023 on OpenAlexafffund
Michael Massimi, Andrea Charise

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsIntersection (aeronautics)HumanismComputer scienceExploratory researchEpistemologyEngineering ethicsSociologyEngineeringSocial sciencePolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

What happens to human-computer "interaction" when the human user is no longer alive? This exploratory paper uses insights from the critical humanist tradition to argue for the urgent need to consider the facts of mortality, dying, and death in HCI research. Using an interdisciplinary approach, we critically reflect upon how the intersection of death and computing is currently navigated and illustrate the conceptual and practical complexities presented by mortality, dying, and death in HCI. Finally, we introduce the concept of thanatosensitivity to describe an approach that actively integrates the facts of mortality, dying, and death into HCI research and design.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0080.044
Scholarly communication0.0070.011
Open science0.0010.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.037
GPT teacher head0.320
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations121
Published2009
Admission routes2
Has abstractyes

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