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Record W4306699925 · doi:10.1177/17506980221126651

‘There is no room in our city for hate’: The re-emerged debates over the current and former place name of a Canadian city

2022· article· en· W4306699925 on OpenAlexaboutno aff
Jason F. Kovacs

Bibliographic record

VenueMemory Studies · 2022
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
Fundersnot available
KeywordsHomelandGermanHistoryState (computer science)Subject (documents)Media studiesSociologyPolitical scienceLawPoliticsArchaeology

Abstract

fetched live from OpenAlex

In 1916, Berlin, Ontario, disappeared off the map through a controversial vote. In its place came a new toponym named after British Secretary of State for War, Horatio Herbert Kitchener. Over a century later, a Facebook post about the city’s name gained local media attention. For the writer of the post, the name Kitchener was synonymous for hate due to the military figure’s role in expanding the use of internment camps during the Second Boer War. However, the Berlin–Kitchener controversy is far older than the recent news story; it goes back to 1991 when a news article brought up the subject a year after the city’s German-Canadian community celebrated the reunification of their cultural homeland. This article examines the original resurfaced controversy over the 1916 name change as well as the recently re-emerged debate. It is argued that the origins of both debates are markedly different and reflect different concerns.

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)0.005
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.467

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0370.059
Scholarly communication0.0140.008
Open science0.0020.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.000

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 head0.081
GPT teacher head0.349
Teacher spread0.267 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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