Hey folks! 👋 I've been diving deep into SciSpace recently for a literature review, and I keep hitting the same snag: my search results are getting cluttered with articles from what look like predatory or low-quality journals. It's adding a ton of manual filtering overhead to my workflow.
I'm wondering how others are tackling this. Are there specific search syntax tricks, filters, or external tools you're using *in conjunction with* SciSpace to clean up your results? My goal is to streamline the process—akin to setting up a good CI pipeline where you automatically ignore noise from the start.
Here's what I've tried so far, with mixed success:
* Using the built-in filters for "Publication Year" and "Citation Count" helps a bit, but it's not a perfect proxy for quality.
* Manually adding `-"[Journal Name]"` style exclusions in the search bar for known predatory publishers. This is effective but tedious, like maintaining a long blocklist in a config file.
* I've thought about exporting results and running them through a custom script that checks against lists like Cabell's Predatory Reports, but that breaks the integrated flow.
Has anyone found a more elegant, automated approach within SciSpace itself? Maybe a community-maintained list of reputable journals we could use as an inclusion filter? I'd love to hear your hacks and pipelines for this.
-pipelinepilot
Pipeline Pilot
Ah, the dream of a clean, automated filter. I'm a bit skeptical that the "elegant, automated" solution you're hoping for exists, at least not without some major trade-offs.
You're essentially asking the platform to make a quality judgment for you. Who gets to decide what's predatory? A curated list? That introduces its own bias and lags behind new journals. An algorithm? Good luck getting that right without accidentally filtering out legitimate niche or emerging publications.
That manual blocklist approach might be tedious, but it's probably the most precise tool you have. It's like product feature flags - sometimes a simple, explicit on/off switch is better than a complex heuristic that fails in edge cases.
But what about the edge case?