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Stay the Course or Seize an Opportunity? Options for Alberta’s Post-Secondary Institutions in a Period of Uncertainty About the Rebound of the Oil Economy

2017· article· en· W6884660965 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsOil boomRestructuringBoomCertaintyCommodityPeriod (music)Petroleum industryOil price

Abstract

fetched live from OpenAlex

Colleges and universities in Alberta feel the booms and busts of the oil-driven economy, too. When oil prices are high, and oil exploration and new project construction are booming, post-secondary institutions will often find themselves unable to keep up with the demand for the education and skills-training programs that employers are clamouring for, with fewer spots available for students than there are students eager to fill them. When oil prices drop, and exploration and construction dry up, the schools face the opposite problem: They have too much capacity in the kinds of programs for skills that traditionally serve those sectors directly connected to oil, or closely linked to them, where there is suddenly a glut of available labour. Making matters particularly complicated is that when oil prices fall, there is never any certainty of when they will rebound. If the lower oil prices are short lived like after 2009, colleges and universities have needed only to be patient and ride out shortterm disruptions, without the need to restructure their program offerings. However, that was not the case after 1985, where oil prices stagnated for an extended period of time. Now, some observers project that the decline in oil prices that began in 2014, with prices yet to fully recover, could last even longer, perhaps with oil becoming the “new coal” and remaining in glut indefinitely. Not knowing whether oil prices will rebound sooner, later, or never puts Alberta’s post-secondary institutions in a tricky situation. Their programs providing skilled workers to the province’s oil-based economy are longstanding and well-respected and the prospect of shrinking them or dismantling them, and shifting a school’s focus to different programming priorities, should not be taken lightly as it could be very expensive to reverse if oil prices do indeed end up rebounding. But if they do not, they will nevertheless face pressure to do so, anyway, due to the considerable resources being tied up by programs that are not in high demand. If post-secondary administrators and governors cannot know when oil prices will rebound, if ever, they are even less able to predict what sectors Alberta’s future economy will shift toward as it diversifies away from its energy export reliance. Whatever decision is made, to stay the course or shift to exploit expected opportunities, university and college leaders are taking risk where the consequences will be borne across the institutions’ students and faculty and the Alberta taxpayer. In that light there is a larger, existential question that must be addressed when considering Alberta post-secondary education institutions and how they respond to the slumping energy sector. What is the mission of PSE institutions in the Alberta economy? Are they instruments of economic adjustment, providing education and skills training that allow Albertans to be mobile across jobs, employers, industries and regions? Or, are they instruments for fostering economic diversification, where research, education and skills training are oriented toward meeting the needs of a targeted or emerging economic opportunity?

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.932

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.007
Scholarly communication0.0130.004
Open science0.0020.007
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0490.005

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 head0.045
GPT teacher head0.294
Teacher spread0.250 · 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
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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