Bibliographic record
Abstract
This chapter raises a socio-cultural critique of the ‘dehumanizing ideologies’ workplace e-learning scenario. Dehumanizing ideologies operationalize the workforce in the workplace through strategic priorities, value chains, and business processes. The workplace e-learning scenario of dehumanizing ideologies precipitates around the instantiation of three concepts: information and communication technologies (ICTs), knowledge, and commodification. An exploratory case study looks at Human Capital Theory. The basic assumptions on economics, knowledge, and people which permeate and sustain this socio-economic view are questioned. These pursuits result in a dichotomous worker (when people are considered as capital and, as such, separable from their knowledges). Unquestioned, socio-cultural assumptions and consequences now facing and evaluating the workforce also become known as are the pedagogical outcomes of a workplace e-learning that is interpreted by human capital theory and its concomitant ideologies. Socio-cultural insensitivity from workplace e-learning, in this scenario, comes from the basic, unquestioned assumption that workers are essentially socially flawed and culturally inferior; accountable for overcoming their sociocultural flaws and inferiorities; and, need to be operationalized by workplace e-learning, through strategic priorities, value chains, and business processes, to meet the expectations of the infallible and commodified workplace. The recurring confluence of commerce, technology, and government, all now become visible as they ideologically mould global, knowledgebased economic policies which in turn influence local knowledge management practices and apparatuses. Organizations that wish to participate in global, knowledge-based economies readily comply. Workplace e-learning now becomes another ideological instrument for the ideological pursuits of commodified knowledges from an operationalized and dehumanized subject within 21st century organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.090 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".