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Record W2479615004 · doi:10.5539/ibr.v9n9p122

The Adjustment of Maltese Firms to the Post-crisis Economic Environment: Evidence from a Firm-level Survey

2016· article· en· W2479615004 on OpenAlexvenueno aff
Brian Micallef

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicUnemployment and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceMalteseEconomic shortageProbit modelEconomicsFinancial crisisMultivariate probit modelLabour economicsBusinessMonetary economicsDemographic economicsMacroeconomicsEconomic growthEconometrics

Abstract

fetched live from OpenAlex

In contrast to the experience of southern and peripheral economies in the euro area, Malta has weathered the financial crisis relatively well and its labour market remained resilient in the face of shocks. Using information from a firm-level survey conducted in 2014, this paper focuses on the nature of the shocks hitting the economy after the crisis and the reaction of Maltese firms to these shocks. Concerning the latter, a distinction is made between the firms’ decisions to adjust their workforce and on the wages given to new hires compared to incumbents. The empirical analysis is conducted with a multivariate probit framework that controls for both firm and workforce specific characteristics as well as the nature of the shocks faced by the firms. The results highlight the high degree of heterogeneity in demand conditions across sectors although concerns about skill shortages were broad-based.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.169
GPT teacher head0.318
Teacher spread0.150 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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