A Policy Discourse Analysis of Academic Probation in Dominican Universities
Bibliographic record
Abstract
An issue universities encounter is students not meeting institutional academic requirements, otherwise termed minimum grade point averages.As part of the solution to the aforementioned problem, institutions rely on academic probation policies to inform retention practices.These policies belong to a long history of student grouping and classification based on academic institutional requirements.Although the emergence of academic probation is unclear, it can be traced in research since the 1920s (Held, 1941;May, 1923;Reeder, 1942; Stone, 1920).In the present, academic probation is still considered a policy that groups and classifies students based on institutional academic requirements (Arcand & Leblanc, 2012;Arcand, 2013).Previous research has approached students' perspectives with academic probation and has found that these are affected in terms of their beliefs in their academic capabilities, and has emotional repercussions (Barouch, 2017;Duffy, 2010;Sage, 2010).Nevertheless, understanding how policies guide institutional behavior and its intentional and unintended consequences might be helpful.Studies related to academic probation policies and their role as a solution to the issue of students not meeting institutional academic requirements are scarce.For this reason, the purpose of this study was to uncover the recurring themes of academic probation policies in Dominican universities. Theoretical Framework PowerPower "is produced and transmitted through knowledge and discourse at the micro level of society," while at the macro level "ideologies, structures and institutions" are used to focus and transmit power (Iverson 2010, p.196).Power can be exercised through techniques of surveillance (use of experts to monitor and increase efficiency), (self)regulation (explicit use of regulation to invoke a rule, often through the use of rewards and punishment), normalization (comparisons to invoke conformity to a standard), and classification (ways in which groups and individuals are differentiated from one another through sorting and ranking of identity statuses) (Iverson, 2010). MethodPolicy analysis is considered a combination of "critical approaches with methods of textual analysis that allows for an analysis of text that focuses on silences and exclusions, while at the same time giving voice to those at the margins" (Iverson, 2010, p. 195).The method for this
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".