Bibliographic record
Abstract
Individuals' identities are influenced by the social systems they belong to and, therefore, the organizations they work for (Allen, 2005, p. 35; Ashman & Gibson, 2010, p. 133). Many organizations attempt to influence employees’ actions through internal branding (Handelman, 2009, p. 51; Mesmer-Magnus, 2012, para. 5, Mitchell, 2002, pp. 100-105). Internal branding may include the use of manipulation through marketing to influence employees’ values and perceptions of the organization (Handelman, 2009, p. 51; Mesmer-Magnus, 2012, para. 5, Mitchell, 2002, pp. 100-105). Since social systems influence social identity, internal branding that uses manipulation may influence individual identity (Allen, 2005, pp. 35-36). Internal branding also includes the organization requiring employees to adopt brand-congruent behaviours in their interactions (Löhndorf & Diamantopoulos, 2014, Employee Brand-Building Behaviors section, para. 2). Brand-congruent behaviours usually require emotional labour, which has been shown to have psychological and physical ramifications (Mesmer-Magnus et al., 2012, ‘Discordant emotional states’ section). Internal branding is also expected of employees outside of work due to social media surveillance by their organization (Jeske & Shultz, 2016; Madera, 2012, as cited in Sutherland et al., 2019). This paper explores the existing scholarship to understand the impact of internal branding on the identity of employees and how it could create mentally unsafe working conditions for employees.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.028 |
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".