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Record W2128938413 · doi:10.1787/5k8x6l198524-en

Leveraging Training and Skills Development in SMEs

2012· paratext· en· W2128938413 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOECD local economic and employment development (LEED) working papers · 2012
Typeparatext
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsFlexibility (engineering)ProductivityTraining (meteorology)BusinessSmall and medium-sized enterprisesIndustrial organizationMarketingKnowledge managementEconomic growthManagementEconomicsComputer scienceFinanceGeography

Abstract

fetched live from OpenAlex

This paper looks at a study carried out among 80 small and medium sized enterprises (SMEs) in two Canadian cities, Montréal and Winnipeg, based on a survey and case studies, which show the importance of innovation among Canadian SMEs. These innovations in turn create new demands for skill development, both through formal training and in informal activities. The outcomes of the study show two significant trends. First, an uneven development of learning activities among SMEs is related not only to the size of firms, but also to their orientation towards innovation and shared productivity measures. Second, because they do not have enough internal resources and flexibility to drive productivity growth through learning and training by themselves, SMEs need some form of group based mechanisms to solve this structural problem. However, it is noted that participation of unskilled employees in both formal and informal learning remains an important challenge for the great majority of SMEs.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.029
GPT teacher head0.226
Teacher spread0.197 · 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