Bibliographic record
Abstract
This theoretical study contributes to the research of pharmaceutical companies’ HR Management practices, and more specifically their staffing systems, through close examination of Novartis Consumer Health Division’s recruitment system. After an overview of the firm’s overall staffing philosophy, external and internal recruitment are also reviewed. E-Recruitment is explored due to its significant place in today’s staffing methods, as a result of technological advancements. Last but not least, the Equal Employment Opportunities of Novartis are examined, since Human Rights and Diversity & Inclusion receive great attention nowadays in most companies’ HR Management strategies. The findings of this study for each section are based on a comparison of the information which was provided through interviews of HR Department employees of the company, as well as my individual research, with the literature review. The most important conclusions that were drawn were, first, that Novartis closely follows Schneider’s Attraction-Selection-Attrition (ATA) model when it comes to its Person-Organization versus Person-Job fit, and second, that one of the main methods through which the company achieves organizational effectiveness, is through successful alignment of its HR systems with those of the other departments/line managers. Finally, recommendations for future research are also provided.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".