MétaCan
Menu
← Retour à la cohorte
Enregistrement W6976504141 · doi:10.6068/dp1502f18f76953

TREND: United States Census Bureau, Bureau of Labor Statistics. Current Population Survey: Labor Force Statistics: Unemployed | Seasonally Adjusted: Seasonally Adjusted | Demographic Indicator: Unemployment Level - 16-19 yrs., Black or African American Women, 1972/1 - 2015/2. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 002-026-010

2015· other· en· W6976504141 sur OpenAlexaboutno aff

Notice bibliographique

RevueData Planet · 2015
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCensusCurrent Population SurveyUnemploymentPopulationWork (physics)American Community SurveyQuarter (Canadian coin)Discouraged workerSample (material)

Résumé

récupéré en direct d'OpenAlex

United States Census Bureau, Bureau of Labor Statistics. Current Population Survey: Labor Force Statistics: Unemployed | Seasonally Adjusted: Seasonally Adjusted | Demographic Indicator: Unemployment Level - 16-19 yrs., Black or African American Women, 1972/1 - 2015/2. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 002-026-010 Dataset: Reports estimates of the civilian noninstitutional population ages 16 and older that are unemployed and looking for full-time work. Estimates are segmented by sociodemographic characteristics. Unemployed persons are all those who: 1) had no employment during the reference week; 2) were available for work, except for temporary illness; and 3) had made specific efforts, such as contacting employers, to find employment sometime during the 4-week period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed. Full-time work is defined as working 35 hours or more (at all jobs combined). The Current Population Survey is a monthly survey of the civilian noninstitutional population ages 16 and older conducted with a probability sample of 60,000 households in the United States by the Census Bureau for the Bureau of Labor Statistics. The resulting Labor Force Statistics dataset, presented here, provides a comprehensive body of information on the employment and unemployment experience of the nation's population, classified by age, sex, race, and other characteristics. Data are collected by personal and telephone interviews. The survey reference period is the calendar week (Sunday through Saturday) that includes the 12th day of the month; the actual survey is conducted during the following week, ie, the week containing the 19th day of the month. Basic labor force data are gathered monthly; data on special topics are gathered in periodic supplements. Persons less than 16 years of age are excluded from the official estimates because child labor laws, compulsory school attendance, and general social custom in the US severely limit the types and amount of work that children under age 16 can do. (Prior to 1948, the sampled population included those ages 14 and older.) Persons on active duty in the US Armed Forces are excluded from coverage, as is the institutional population, which consists of residents of penal and mental institutions and homes for the aged and infirm. Seasonally adjusted and nonadjusted estimates are included in the dataset. Seasonal adjustments make it easier to observe the cyclical and other nonseasonal movements in the series. In evaluating changes in a seasonally adjusted series, it is important to note that seasonal adjustment is merely an approximation based on past experience. Seasonally adjusted estimates have a broader margin of possible error than do the original data on which they are based, because they not only are subject to sampling and other errors but also are affected by the uncertainties of the seasonal adjustment process itself. Since January 1980, national labor force data have been seasonally adjusted with a procedure called X-11 ARIMA (Auto-Regressive Integrated Moving Average). Statistics are presented for the nation in total, by the month, quarter, and year, where available. http://download.bls.gov/pub/time.series/ln/ Category: Population and Income, Labor and Employment Subject: Labor Force Status, Unemployed Workers, Unemployment, Civilian Labor Force, Sociodemographic Characteristics, Employment Status Source: Bureau of Labor Statistics The Bureau of Labor Statistics (BLS) of the United States Department of Labor is the principal fact-finding agency for the federal government in the broad field of labor economics and statistics. The BLS is an independent national statistical agency that collects, processes, analyzes, and disseminates essential statistical data to the American public, the US Congress, other federal agencies, state and local governments, business, and labor. The BLS also serves as a statistical resource to the Department of Labor. http://www.bls.gov/

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,133
Score d'incertitude au seuil0,266

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,012
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0050,018
Études des sciences et des technologies0,0010,000
Communication savante0,0020,003
Science ouverte0,0030,002
Intégrité de la recherche0,0010,004
Charge utile insuffisante (le modèle a refusé de juger)0,0800,090

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,072
Tête enseignante GPT0,342
Écart entre enseignants0,269 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueData Planet→Travaux en français237 207→