Awakening: “The Sociological Imagination” Through Creativity
Bibliographic record
Abstract
For my SaPP, I was asked to research what creativity is, why it is important to students, and create activities for students to strengthen their creative skills. To form my activities, I used a sociological lens, but also made my activities generic so that any professor could use my work. Specifically, I used the sociological imagination to develop my activities. C. Wright Mills, coined the term “the sociological imagination”, he explains that we must use our imagination to understand why personal issues are connected to greater issues we face as a society. Through my activities, students will face personal challenges and be encouraged to reflect on how these challenges affect broader society. My activities can be used during tutorials or lectures. To break this assignment down I developed a video about what creativity is and why it is important for students, an instruction manual for creative class engagement activities, and a deck of 12 creative class engagement activities to strengthen students' creative skills. In my work I discovered creativity is not an innate skill, we are not born with it, but it can be learned and strengthened. My findings concluded that engaging in creativity effectively is a process that requires observation, trial and error, and collaboration. Through this process, students can become critical thinkers, gain resiliency, and develop strong problem-solving skills.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 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".