MétaCan
Menu
Back to cohort
Record W6888946010 · doi:10.25316/ir-13975

Here today, here tomorrow: A history of Port Alberni

2001· other· en· W6888946010 on OpenAlexaboutno aff

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2001
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)FishingPresentation (obstetrics)Gold rushIndigenousWhite (mutation)

Abstract

fetched live from OpenAlex

Audio recording and transcript of Jan Peterson's February 2001 presentation to the Nanaimo Historical Society about the history of Port Alberni. Peterson, an author of multiple books about the Alberni Valley, focuses her talk on the area’s history from 1856, when Adam Grant Horne was the first white person to explore the region, to 1967, when the “twin cities” of Alberni and Port Alberni amalgamated to form a single municipality. Peterson chronicles the region’s long standing involvement with the forestry industry, starting in 1860, when workmen arrived to build the Anderson sawmill. Early settlers, transportation, newspapers, politics, mining, and fishing in the area are also covered. Peterson details several notable event’s in Port Alberni’s history including: the “Great Trek” of 1934, where striking loggers marched from Parksville to Great Central Lake; the June 23, 1946 Vancouver Island earthquake; and the devastating March 27, 1964 tsunami. Peterson discusses the lead up to amalgamation between Alberni and Port Alberni, including the tenure of the first woman mayor on Vancouver Island, Mabel Anderson of Alberni, who opposed the merger. Peterson concludes her talk with a brief update about Port Alberni since amalgamation, focusing on the challenges in the forest industry, including decreased demand and environmental and Indigenous opposition.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.900
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.002
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0440.005

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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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

Citations0
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueVIURRSpace (Vancouver Island University)French-language works237,207