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Record W4400992118 · doi:10.69554/vjhd6183

Building the future of Library and Archives Canada : Assessing readiness for transformational projects

2023· article· en· W4400992118 on OpenAlexaffabout
Scott Hamilton, Katharine Cornfield, S. Sudarsham Rao

Bibliographic record

VenueCorporate real estate journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsTreasury Board of Canada SecretariatLibrary and Archives Canada
Fundersnot available
KeywordsTransformational leadershipEngineering managementLibrary sciencePolitical scienceBusinessEngineeringComputer sciencePublic relations

Abstract

fetched live from OpenAlex

As Canada’s pre-eminent memory institution, Library and Archives Canada (LAC) has the legislative mandate to collect, preserve and make accessible Canada’s priceless documentary heritage. This unique role as public caretaker for a vital, ever-growing collection brings considerable challenges to LAC as a corporate real estate (CRE) organisation. Added to this complexity is LAC’s relatively recent assignment as custodian of its own portfolio of special purpose real property, which represents a fundamental change to the institution’s role and responsibilities. Transfer of custody, which coincided with commencement of two of the Government of Canada’s most innovative major capital construction projects, has touched off a period of significant transformation within LAC. This paper will provide important background and context with respect to LAC’s unique mandate, history and evolution as a federal real property custodian; elaborate on the complexity of CRE management in this context; detail the two major projects; and highlight the guiding principles driving delivery through this transformative time. Lastly, this paper will offer actionable insights for others contemplating organisational readiness to undertake high-profile capital projects.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.010
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 head0.033
GPT teacher head0.284
Teacher spread0.251 · 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 designOther design
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
Published2023
Admission routes2
Has abstractyes

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