A Comparative Analysis Career Drivers Marketing & Financial Professionals using RSI Psychometric Tool
Bibliographic record
Abstract
The paper presents the authors’ own research, which points to the possibility of applying the Richmond Survey Instrument test on the two different profiles of employees (Finance & Marketing) in Pune region. Participants and procedure-The research was conducted in the years 2019-2020 in Pune. The study population comprised 100 individuals from both the profiles. The employees were selected by Non-Probability Convenience Sampling method. The research participants had never undergone psychological evaluation for personality test (for instance, they had never taken the RSI test). The study population comprised 133 males (66.5%) and 67 females (33.5%). Results The statistical procedures applied in the present study allowed us to conduct empirical examination of the indicators of the investigated variables constituting the major psychological criteria for describing psychological functioning of personality, and thus to identify the main Career Anchors of these two professionals. Analysis of the data obtained as a result of this research allowed us to distinguish two significantly different clusters in the group examined individuals. The results of the present investigation indicate that cluster 1 exhibited a higher level of Strong determination to the specific Career Anchors whereas of personality structure compared with the study participants belonging to cluster 2. Keywords: Richmond survey Indicator; Psychological; Career Anchors; Personality
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".