Titlebook: Manpower Training; Theory and Policy Adrian Ziderman Textbook 1978Latest edition Macmillan Publishers Limited 1978 human resource developme

充裕

書(shū)目名稱(chēng)Manpower Training影響因子(影響力)




書(shū)目名稱(chēng)Manpower Training影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Manpower Training網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Manpower Training網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Manpower Training被引頻次




書(shū)目名稱(chēng)Manpower Training被引頻次學(xué)科排名




書(shū)目名稱(chēng)Manpower Training年度引用




書(shū)目名稱(chēng)Manpower Training年度引用學(xué)科排名




書(shū)目名稱(chēng)Manpower Training讀者反饋




書(shū)目名稱(chēng)Manpower Training讀者反饋學(xué)科排名

ASTER

The Economics of Inservice Training, but, utilising the analogy drawn earlier with physical capital, why, predominantly, do firms create their own human-capital resources rather than obtaining them from specialised capital-goods producers as in the case of physical assets?

不足的東西

The Rationale for State Provision of Training,f training? While many arguments are advanced to justify this role of the State in training provision, they may be subsumed under three main headings —market failure, stabilisation and equity considerations; these we discuss in turn.

AUGUR

VERT

Adrian Zidermantion operators. In this way we get new results for generated means which are well-known representatives of aggregation operators. Empirically measured data suggest that people do not understand conjunction necessarily as a commutative operation. Finally, we investigate the modelling of the conjuncti

試驗(yàn)

Adrian Zidermantion operators. In this way we get new results for generated means which are well-known representatives of aggregation operators. Empirically measured data suggest that people do not understand conjunction necessarily as a commutative operation. Finally, we investigate the modelling of the conjuncti

警告

Adrian Zidermanoblem can be solved with limited, yet arbitrary, precision..Further, we show that probability in Markov Decision Processes can be viewed as a degrading quality and DLTL as a probabilistic linear temporal logic with quantitative operators. We discuss expressiveness of DLTL as compared with expressive

HEAVY

Adrian Zidermanunctionals. In this work, we study the possibility of using the S-curve with DCE-MR data. We select the spatial regularization parameter using the S-curve, leaving the temporal regularization parameter as the only free parameter in the reconstruction problem. In this work, the temporal regularizatio

muster

Adrian Zidermanunctionals. In this work, we study the possibility of using the S-curve with DCE-MR data. We select the spatial regularization parameter using the S-curve, leaving the temporal regularization parameter as the only free parameter in the reconstruction problem. In this work, the temporal regularizatio

BOLT