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El cese de relación laboral en Ecuador por causa del Covid 19

2020· article· es· W3095857544 on OpenAlexvenueno aff
Ligia Maricela Niama Rivera, Carlos Iván Villalva Heredia, Mónica Paulina Terán Pérez, Edison Fernando Campos Collaguazo

Bibliographic record

VenueConcienciaDigital · 2020
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesCoronavirus disease 2019 (COVID-19)Political sciencePhilosophyMedicine

Abstract

fetched live from OpenAlex

La revisión de información de estos 6 meses a partir del inicio de la pandemia en nuestro país tiene como presente apunte de incumbencia recoge una posible marca sobre la cuál podría ser el trastazo colectivo provocado por el real arrebato sanitario del COVID19. En base a las estadísticas registradas en estamentos como ministerio de trabajo, tanto en desafiliaciones a la seguridad Social y en base a la aparente emoción sobre el oficio (analizando sus características y madrigal se determinan cuáles serán los colectivos más perjudicados y/o aquellos que se encuentran en un ámbito laboral más delicado. El coronavirus está alcanzando a más víctimas en Ecuador entre ellas se trata de las empresas sin flujo de caja y los trabajadores que pierden su empleo por falta de liquidez. El país presentaba una tasa de subempleo y desempleo del 24.6 por ciento en diciembre de 2019. Siendo este que en su actualidad cuenta con 17.5 millones de habitantes y cerca de 8.5 millones son económicamente activos, pero no todos producen ni perciben un salario formal, la crisis que enfrentamos por esta pandemia se ha agravado por el quiebre de empresas durante los últimos meses, calculando que cerca de 80.000 trabajadores de empresas públicas y privadas, han sido cesados en sus funciones y están en casa sin salario, ni indemnización por despido

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)0.001
metaresearch head score (Gemma)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.038
GPT teacher head0.247
Teacher spread0.209 · 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; both teacher heads agree on what is shown here.

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
Published2020
Admission routes1
Has abstractyes

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