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Record W2768641390 · doi:10.5130/csr.v23i2.5472

Ghosting Politics: Speechwriters, Speechmakers and the (Re)crafting of Identity

2017· article· en· W2768641390 on OpenAlexaboutno aff
Michael Richardson

Bibliographic record

VenueCultural Studies Review · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLiminalityPoliticsIdentity (music)Identity politicsMedia studiesSociologyRelation (database)PropositionLawAestheticsPolitical scienceEpistemologyArtAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

Despite public awareness of their role, speechwriters occupy an anxiously liminal position within the political process. As the ongoing dispute between former Australian prime minister Paul Keating and Don Watson over the Redfern Speech suggests, the authorship and ownership of speeches can be a fraught proposition, no matter the professional codes. Crafting and re-crafting identity places speechwriter and speechmaker in a relation of intense intimacy, one in which neither party may be comfortable and from which both may well emerge changed. Having written speeches for Jack Layton, former leader of the New Democratic Party of Canada, I know just how complex, uncertain and productive that relation can be. This article conceives of identity as transindividual, formed in the intensity and flux of encounter, and weaves together the personal and the critical to examine politics’ speechwriting ghost.

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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0060.025
Scholarly communication0.0120.008
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.230
GPT teacher head0.396
Teacher spread0.165 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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