Reflection on digital design for marketing - Summer 2018 with Simon & Schuster Canada
Bibliographic record
Abstract
This report examines the 2018 summer digital marketing campaign by Simon & Schuster Canada, during this report’s author’s professional placement. The core message of the report is that digital design skills are more necessary than ever for publishers. Firstly, an overview of the Simon & Schuster parent company is offered, but the focus will be on the Canadian branch’s team in Toronto, Ontario. Secondly, the 2018 summer campaign will be discussed, with notes on the 2017 campaign and lessons for the 2019 campaign, and successful digital assets built in support of the 2018 summer campaign will be shared. Thirdly, two 2018 spring titles’ digital marketing plans will be featured as case studies: Ocean Meets Sky by the talented illustrator brothers Terry & Eric Fan for the Children’s section, and Come From Away by Genevieve Graham for the Adult one. The report concludes with a brief examination of reasons why publishing industry hopefuls need to include digital design skills in their professional repertoire. All figures and stats are accurate as of March 2019.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.111 | 0.020 |
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".