Alberta/Haskayne Executive MBA Program
Bibliographic record
Abstract
Abstract. On a typical workday, how many e-mail messages, faxes, and telephone calls do you receive? How many times are you interrupted at work? In this session, participants will complete an exercise, and then the presenters will facilitate a discussion about information overload causes, symptoms and countermeasures. One recent study found that the average North American knowledge worker receives more than 142 communications per day; in the form of e-mail messages, phone calls, face-to-face interruptions, etc. Spira (2005) estimates that unnecessary interruptions consume 28 percent of the average knowledge worker’s day, imposing 28 billion lost hours on American companies each year, at an annual cost of $588 billion. What is Information Overload? Eppler and Mengis (2004) define information overload as simply “receiving too much information. ” The popular notion that information is power, that more information means higher performance, operates only up to a point. Beyond this point, people become overloaded, and more information leads to performance deterioration. It is important to understand causes and symptoms of information overload, along with possible countermeasures to ease it. Causes. According to Kock (2000), the causes of information overload include: time pressure,
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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; both teacher heads agree on what is shown here.
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".