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Training Requirement and Training Model Institution for Peasant Worker

2010· article· en· W1849767134 on OpenAlexvenueno aff
Pingqing Liu, Fang Liu

Bibliographic record

VenueCross-cultural communication · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsPeasantInstitutionDowntownBusinessTraining (meteorology)Political scienceEconomic growthSociologyPublic relationsEconomicsGeographyLaw

Abstract

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Training for Peasant worker is other foreland task besides Peasant worker ‘s “grant issue in Downtown “ and “proper right issue”. Currently , It is major way that peasant worker ‘s employment base on their relationship network instead of getting relative Job information from government or social organization, besides that It is insufficient that enterprise provide training for peasant worker , that cannot fit into peasant worker ‘s needs under current status that Social Training Organization providing’s training are far behind peasant worker’s requirement. Peasant worker desire training subjectively ,but they are confused in the training content, In fact they are lack motivation to participant training , special on some Skill field of lower return and higher intensity. Based on the fact it is very difficult to set up training institution. It is key that setting up innovative training institution that converting peasant worker’s potential training desirability to reality. The key points of institution innovation should solve the following issues : Peasant worker should have rights working in downtown; Peasant worker can survive in downtown; Peasant worker can stay in downtown longer time. Converting from potential training desirability to reality will become true if we can help peasant worker eliminating visitor concept in their mind. Key Words: peasant worker, training requirement, institution innovation Resume La formation de la main-d’oeuvre rurale constitue un nouveau probleme apres le probleme de ‘‘acces en ville’’ et le probleme de ‘‘sauvegarde des droits et interets’’ de la main-d’oeuvre rurale. A l’heure actuelle, ils choisissent le metier a l’aide d’un ‘‘reseau de relation’’ et ils obtiennent peu d’informations du gouvernement et des institutions sociales. En outre, dans le contexte de l’insuffisance de la formation de la main-oeuvre rurale offerte par les entreprises et le developpement retarde des institutions de formation sociales convenant a la main-d’oeuvre rurale, ils aspirent subjectivement a la formation, mais ils apparaissent plutot perplexes en ce qui concerne le contenu de la formation et manque de force motrice pour participer a la formation, ils se desinteressent surtour de la formation de technique a forte intensite de travail mais mal payee. Vu le manque de force motrice pour la formation, il est bien difficile d’etablir le systeme de formation dans ce domaine. Il s’agit de l’innovation institutionnelle pour que la demande potentielle de la formation de la main-d’oeuvre rurale se transforme en demande reelle. La cle de l’innovation institutionnelle consiste a leur donner le droit d ‘‘entrer’’, ‘‘rester’’et ‘‘vivre’’en ville. L’elimination de la pensee de ‘‘hote de passage’’ conduit forcement a la transformation de la demande. Mots-cles: la main-d’oeuver rurale , la demande de la formation, l’innovation institutionnelle 摘 要 農民工培訓是繼農民工在城鎮“准入問題”以及“權益維護問題”之後的又一前沿課題。當前,農民工選擇職業主要借助“關係網絡”而較少從政府以及社會機構那裏獲得相關的資訊,加之在企業爲農民工提供培訓不足以及適合於農民工的社會培訓機構發育滯後的情況下,農民工主觀上渴求培訓,但他們在培訓內容上顯得有些迷茫,實際參與培訓的動力並不足,特別是對於勞動強度大、報酬不高的技能培訓興趣淡漠。在農民工缺乏培訓動力的情況下,相應的培訓體系是難以建立起來的。農民工培訓的潛在需求轉變爲現實需求在於制度創新。制度創新的著力點在於讓農民工在城市“進得來”、“留得下”、“住得久”。農民工“過客”心理的消除,必將帶來他們潛在培訓需求向現實需求的轉化。 關鍵詞:農民工;培訓需求;制度創新

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.004

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 head0.115
GPT teacher head0.355
Teacher spread0.240 · 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 designQualitative
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
Published2010
Admission routes1
Has abstractyes

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