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<i>La différence</i>: condition of exclusion or of reconnaissance?

2008· article· en· W2128016320 on OpenAlexaff
Danielle Blondeau

Bibliographic record

VenueNursing Philosophy · 2008
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanityDignityCivilizationBarbarismHumanismSociologyDenialEnvironmental ethicsAestheticsLawPsychologyPolitical sciencePsychoanalysisPhilosophy

Abstract

fetched live from OpenAlex

From the Middle Ages onto the 19th century, following the trend set in leper hospitals, madness was to be hidden, secluded in dark places, far away from the mainstream of society. The emergence of the mad person, perceived as inevitably different, allows to make the boundaries between reason and folly, between human and inhuman, irrelevant. If leper hospitals have almost emptied out, if there are much fewer confinement facilities, the values and images related to the leper or the mad person, as well as the sense of exclusion, continue to persist. The purpose of this paper is to show clearly that this matter of exclusion is a serious legacy that could very well apply nowadays to other figures that, each in their own way, symbolize menace or mockery. It applies notably to the aged and the dying who both appear as the opposite of modern society and its values of efficiency, productivity and profitability. The multiplication of places where old people are left to die, and the elderly who are crowded in old folks homes, stand as proof of their exclusion from society. Nevertheless, youth and old age coexist, as well as life and death. If care of others is the trait of a humane civilization, must it be understood that barbarism consists in ignoring its own humanity as well as that of others? In view of such practices of exclusion, policy statements based on recognition of human dignity, where ethical obligation rests on recognition of others and humanism, are rather paradoxical. Is this a paradox or a deadlock; a condition of exclusion or of reconnaissance?

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.437

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 studies0.0010.001
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 head0.109
GPT teacher head0.414
Teacher spread0.305 · 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 designQualitative
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

Citations9
Published2008
Admission routes1
Has abstractyes

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