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Record W2480072890 · doi:10.1057/9780230116436_9

Public-Private Partnerships in the Provision of Infrastructure to Redress the Human Resource Shortages in Zimbabwe

2011· book-chapter· en· W2480072890 on OpenAlexaff
Helen Moatshe

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2011
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAfrican studies and sociopolitical issues
Canadian institutionsThe North South Institute
Fundersnot available
KeywordsRedressGovernment (linguistics)Economic shortagePrivate sectorHuman resourcesIndependence (probability theory)BusinessPublic sectorEconomic growthAgricultureResource (disambiguation)Psychological interventionPolitical scienceEconomicsEconomyGeographyLaw

Abstract

fetched live from OpenAlex

The Government of National Unity (GNU) in Harare suffers from a limited capacity in terms of human resources and skilled personnel, which are key in rebuilding all sectors of the economy. This chapter places emphasis on a holistic approach that gives a historic perspective of the problem, looking backward to 1979 and prior to the country’s independence. It also looks at the role regional countries played in exacerbating the human resource challenges in the postcolonial era and assesses what interventions would be needed to create an enabling environment. A closer look is taken at the economic sector nodes, such as agriculture, that could make a difference in stimulating economic growth. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0040.005
Open science0.0000.004
Research integrity0.0010.002
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 head0.094
GPT teacher head0.312
Teacher spread0.218 · 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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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